ASSESSING MEASUREMENT EQUIVALENCE OF ENGLISH AND …/67531/metadc4315/m2/... · Ernest Harrell, Chair of the Department of Psychology C. Neal Tate, ... Supervision, Leadership, Job

APPROVED: Michael Beyerlein, Major Professor Douglas Johnson, Chair of Graduate Studies in

Industrial/Organizational Psychology David Allen, Thesis Committee Member Ernest Harrell, Chair of the Department of Psychology C. Neal Tate, Dean of the Robert B. Toulouse School of

Graduate Studies

Mikhail Koulikov, B.A.

Thesis Prepared for the Degree of

MASTER OF SCIENCE

UNIVERSITY OF NORTH TEXAS

August 2003

ASSESSING MEASUREMENT EQUIVALENCE OF ENGLISH AND SPANISH

VERSIONS OF AN EMPLOYEE ATTITUDE SURVEY USING MULTIGROUP

ANALYSIS IN STRUCTURAL EQUATION MODELING

Koulikov, Mikhail, Assessing measurement equivalence of the English and Spanish

versions on an employee attitude survey using Multigroup Analysis in Structural Equation

Modeling. Master of Science (Industrial/Organizational Psychology), August 2003, 198 pp.,

13 tables, 18 illustrations, references, 68 titles.

The study utilized the covariance structure comparison methodology – Multigroup

Analysis in Structural Equation Modeling – evaluating measurement equivalence of English and

Spanish versions of an employee opinion survey. The concept of measurement equivalence was

defined as consisting of four components: sample equivalence, semantic equivalence, conceptual

equivalence and scalar equivalence.

The results revealed that the two language versions of the survey exhibited acceptable

measurement equivalence across five survey dimensions Communications, Supervision,

Leadership, Job Content & Satisfaction and Company Image & Commitment. Contrary to the

study second hypothesis, there was no meaningful difference in opinion scores between English-

speaking and Spanish-speaking respondents on the latent construct of Job Content &

Satisfaction.

ACKNOWLEDGEMENTS

This research would not be possible without my business mentor and partner David Allen

who, throughout the project, contributed to the generation of ideas. My professor Michael

Beyerlein was a conceptual simulator, a challenger and a moral supporter for the entire duration

of the project. My wife Rhea was my introspective language critic, which, given that English is

not my native tongue, was instrumental in producing this work. Thank you all very, very much.

ii

TABLE OF CONTENTS

LIST OF TABLES......................................................................................................................... V

LIST OF ILLUSTRATIONS........................................................................................................ VI

LITERATURE OVERVIEW.......................................................................................................... 1

A Case for Globalization: Universal vs. Local ........................................................................... 1 Ethnocentric Approach and Equivalence of Measures ............................................................... 4

Sample equivalence ................................................................................................................ 5 Semantic equivalence.............................................................................................................. 5 Conceptual equivalence .......................................................................................................... 7 Normative equivalence ........................................................................................................... 8

Using Structural Equation Modeling in Establishing Measurement Equivalence.................... 12 SEM: Process Steps .................................................................................................................. 14

Model Specification and Identification................................................................................. 14 Assessment of Overall Model Fit to the Data....................................................................... 17 Assessment of Specific Model Parameters as Related to Overall Model Fit ....................... 26 Multigroup Analysis ............................................................................................................. 27 Assumptions for Analyzed Data ........................................................................................... 27

Using Item Response Theory in Establishing Measurement Equivalence ............................... 30 Literature Overview Conclusion............................................................................................... 33 The Current Study..................................................................................................................... 34

Hypothesis 1.......................................................................................................................... 35 Hypothesis 2.......................................................................................................................... 36

METHOD ..................................................................................................................................... 37

Participants................................................................................................................................ 37 Sample Equivalence.................................................................................................................. 37 Measurement Instruments and Conceptual Equivalence .......................................................... 38 Data Assumptions ..................................................................................................................... 40

Missing Values...................................................................................................................... 40 Multicollinearity ................................................................................................................... 41

Model Specification and Identification..................................................................................... 42 SEM Analysis Procedure .......................................................................................................... 45

RESULTS ..................................................................................................................................... 47

Descriptive Statistics................................................................................................................. 47 Survey Items ......................................................................................................................... 47 Survey Dimensions ............................................................................................................... 48

Baseline Model � Single-Group Analysis................................................................................. 49 Multigroup Analysis � Phase 1 ................................................................................................. 52 Multigroup Analysis � Phase 2 ................................................................................................. 55 Multisample Model Parameters Contributing to Reduction in Model Fit ................................ 59 Comparison of Job Satisfaction Means Between the Language Groups .................................. 60

DISCUSSION............................................................................................................................... 62

iii

Limitations of the Study............................................................................................................ 66 Implications for Research and Practice..................................................................................... 67

CONCLUSIONS........................................................................................................................... 70

APPENDICES .............................................................................................................................. 72

Appendix A............................................................................................................................... 72 Table A1. The English version of the employee opinion survey instrument....................... 72 Table A2. The Spanish version of the employee opinion survey instrument. ..................... 85

Appendix B ............................................................................................................................... 98 Table B1. Factorial Structure of the English version of the survey. Pairwise deletion. ..... 98 Table B2. Factorial Structure of the English version of the survey. Listwise deletion. ..... 99 Table B3. Factorial Structure of the English version of the survey. Mean substitution. .. 100 Table B4. Factorial Structure of the Spanish version of the survey. Pairwise deletion.... 101 Table B5. Factorial Structure of the Spanish version of the survey. Listwise deletion.... 102 Table B6. Factorial Structure of the Spanish version of the survey. Mean substitution... 103

Appendix C ............................................................................................................................. 104 Table C1. English Sample � Missing Data Pattern............................................................ 104 Table C2. Spanish Sample � Missing Data Pattern. .......................................................... 104

Appendix D............................................................................................................................. 105 Table D1. Five survey dimensions� item correlation matrix for the English sample. ....... 105 Table D2. Five survey dimensions� item correlation matrix for the Spanish sample........ 108

Appendix E: EQS output � English sample initial single-group model run ........................... 111 Appendix F: EQS output � English sample - adjusted single-group model run..................... 115 Appendix G: EQS output � English sample - final baseline model run ................................. 120 Appendix H: EQS output � Spanish sample - initial single-group model run........................ 135 Appendix I: EQS output � Spanish sample final baseline model run..................................... 139 Appendix J: EQS output � Initial multigroup model run........................................................ 156 Appendix K: EQS output � Final multigroup model run........................................................ 162

REFERENCES ........................................................................................................................... 193

iv

LIST OF TABLES

Table 1. System of Equation Specifying Model of OC and JS...........................................................13

Table 2. System of Equation Specifying Model of OC and JS...........................................................15

Table 3. Succinct System of Equation Specifying Model of OC and JS. ...........................................15

Table 4. Population Known Parameters Expressed as a Function of Model Unknown Parameters...19

Table 5. Population Parameters Expressed as a Function of OC Model Parameters..........................21

Table 6. OC Model Parameters Expressed Through Population Parameters......................................21

Table 7. Population Parameters as a Function of Parameters of the OC Model.................................24

Table 8. Demographic Composition Comparison of the English- and Spanish-Speaking Samples...38

Table 9. Survey Dimension Reliabilities of the English and Spanish Versions of the Instrument.....39

Table 10. Descriptive Statistics of the of the English- and Spanish-Speaking Data Sets..................47

Table 11. Exploratory Factor Analysis Results for the English- and Spanish-speaking Data Sets ....48

Table 12. Reliabilities of the Chosen Survey Dimensions of the English and Spanish Data Sets......49

Table 13. Single-Survey-Dimension Models� Fit for English and Spanish Samples .........................54

v

LIST OF ILLUSTRATIONS

Figure 1. Relationship model of OC and JS........................................................................................14

Figure 2. Matrix form of equation system specifying model of OC and JS. ......................................17

Figure 3. Covariance matrix of observed variables and residual terms in the model of OC and JS...18

Figure 4. Model unknown parameters expressed through the known population parameters............18

Figure 5. Just-identified model of OC and JS.....................................................................................20

Figure 6. Under-identified model of OC.............................................................................................20

Figure 7. Covariance matrix of observed variables and parameter matrix of the �example� model. .22

Figure 8. Derivation of parameters providing the minimum of the model fitting function. ...............23

Figure 9. A logistic function of the item characteristic curve.............................................................30

Figure 10. Item characteristic curve....................................................................................................31

Figure 11. Six-factor model of cross-language validation of survey measurement equivalence........44

Figure 12. Single survey dimension submodel: Communications......................................................52

Figure 13. Single survey dimension submodel: Supervision. .............................................................53

Figure 14. Single survey dimension submodel: Leadership. ..............................................................53

Figure 15. Single survey dimension submodels: Job content & satisfaction......................................53

Figure 16. Single survey dimension submodels: Company image & commitment. ...........................53

Figure 17. Two-factor model for the Leadership dimension. .............................................................55

Figure 18. Seven-factor multisample study model. ............................................................................55

vi

LITERATURE OVERVIEW

A Case for Globalization: Universal vs. Local

The end of the 20th century was characterized by accelerating globalization in a political

and economic sense (Bairoch & Kozul-Wright, 1998). Deregulation of the markets in the 1970�s,

fall of communism in Eastern Europe in 1980�s and implementation of a new generation of

information technologies in the 1990�s created a stage for new global economic order with the

increasing volume of cross-border trade and flow of resources. Milberg (1998) characterizes

globalization as a phenomenon of capital mobility based on two prongs: foreign direct investment

and international portfolio flows. �[A] true global economy is one which is dominated by

transnational firms and financial institutions, operating independently of national boundaries or

domestic economic considerations.� (Milberg, 1998).

Cross-border financial flows have risen dramatically in the last quarter of the 20th century.

Average foreign exchange market daily trade has grown from $15 billion in the 1973 to $900

billion in 1992 (Bairoch & Kozul-Wright, 1998). Foreign direct investment (FDI) outflows

averaged $50 billion in the early 1980�s and reached $243 billion in the 1990�s (UNCTAD, 1996).

The world stock of outward FDI grew from $282 million in 1970, to $690 million in 1985 to $2.1

trillion in 1993 (UNCTAD, 1995).

The new generation of information technology permitted virtually unlimited and rapid flow

of communications at minimal costs, allowing firms to diversify geographically without

relinquishing managerial control resulting in proliferation of transnational corporations (TNC).

Compared to 7,000 TNCs in 1970, there were about 37,000 parent transnational corporations in

1990�s (UNCTAD, 1995). Accordingly, between 1975 and 1992, the number of employees in

TNCs grew from 40 to 73 million (UNCTAD, 1995).

1

Attracting and retaining talent in various countries and from different cultural and ethnic

groups has become a necessity (Cox & Blake, 1991). Consumer markets are becoming as diverse

as the workforce and there is evidence of culture having a significant effect on behavior of

consumers (Redding, 1990). Effective management of multilingual and multicultural workforce

and services tailored toward various cultural consumer markets has become a source of

competitive advantage (Cox & Blake, 1991).

To monitor the pulse of an organization that has branches in different cultural settings or

employs different cultural groups, one needs to be able to unambiguously compare organizational

processes across all parts of the enterprise and, therefore, across different cultures. A new set of

organizational research requirements is emerging as multinational companies are trying to solve

issues such as: how to gauge the effectiveness of incentive schemes across cultures (Lowe et al.,

2002), to what extent personnel selection practices apply differently in different cultural groups

(Huo et al., 2002), which organizational HR policies and procedures should be universal and

company-wide and which should be local and culture-specific (Von Glinow et al., 2002).

Universal approach in cross-cultural organizational research assumes existence of global

overreaching concepts present in many cultures. Applied in a business setting, this approach is

based on close cross-cultural similarity of understanding and interpretation of organization and

management patterns in companies (Clark, 2000). Existence of standard shared meaning provides

a basis for comparing opinions of individuals from different cultural groups. Traditionally,

operating under assumptions of universalism, concepts and measures have been formulated,

developed and tested in one particular culture (source culture) and then transferred into other

cultural settings (target cultures). This type of research is called ethnocentric or a study from the

etic (culture-common) perspective (Clark, 2000).

2

Contrary to the ethnocentric orientation, the polycentric approach, or research from the

emic (culture-specific) perspective, treats phenomena as being understandable primarily within the

context of a specific culture (Clark & Pugh, 2000). This type or research employs qualitative

rather than quantitative research methods including ethnographic data collection and content

analysis (Clark & Pugh, 2000).

Cross-cultural research has established that both types of research, emic- and etic-oriented,

are legitimate orientations (Dowling, 1999). They address different problems and have different

types of advantages and associated shortcomings. An ethnocentric approach is aimed toward

standardization. By default, it filters out the diversity of understanding of concepts. On the other

hand, in its extreme, study from the etic perspective, or polycentric approach, does not allow for

cross-cultural comparisons because measurement concepts are uniquely understood within the

framework of a particular culture and have little, if any, frame of common reference (Clark &

Pugh, 2000). It makes sense to treat the ethnocentric and polycentric extremes as poles of a

continuum striking to conduct useful cross-cultural comparisons without losing sight of diversity

issues. For example, Raaij developed a continuum of measurement anchored by absolute emic

and etic measures and having two middle points derived from the interaction of the two (Sin,

Cheung and Lee, 1999). Still, an ethnocentric approach is a prevailing method of study. A recent

review of research of international human resource management practices (HRM) showed that

between 1977 and 1997 fifty-nine percent of HRM studies were conducted using the etic

perspective, 27% were emic-oriented and 14% used a combination of both techniques (Clark,

Grant & Heijltjes, 2000).

3

Ethnocentric Approach and Equivalence of Measures

Rapid globalization and requirement for quick integration and adaptation of organizational

processes make the etic perspective especially useful to human resource practitioners (Shenkar,

1995). However, one of the strong criticisms of the etic approach is its ethnocentric bias

(Boyacigiller et al., 1991). The concepts, measures and instruments developed in one culture are

often assumed to be appropriate and applicable in other cultures without verification (Clark &

Pugh, 2000). The bigger question is to what extent certain theories of human behavior and

organizational functioning such as principles of leadership, motivation and decision making

developed in one culture are applicable in other cultures. Most modern organizational theories

have been developed in the USA (Boyacigiller et al., 1991). The research of how precisely those

theories apply in different cultural contexts is not yet widely available (Adler, 2001). Such

research would require a methodologically sound cross-cultural validation of methods and

measurement instruments to prove or disprove applicability of US theories abroad or

transferability of Western organizational theories to, for example, Asian cultures. Therefore,

establishing cross-cultural validity of measures is a fundamental requirement of cross-cultural

study methodologies. The emerging body of research suggests that not addressing the issue of

potential non-equivalence of measurement instruments can result in misleading conclusions about

compared cultural groups (Adler, 1983, Riordan & Vandenberg, 1994; Singh, 1995; Yoo, 2002).

The emerging requirement in ensuring that measurement instruments are directly comparable

across different cultures is establishment of equivalence of measurement tools in every culture it is

applied. There are a few types of equivalence covering the spectrum of requirements necessary

for cross-cultural comparisons: sample equivalence, semantic equivalence, conceptual

equivalence, normative equivalence and scalar equivalence (Behling & Law, 2000).

4

Sample equivalence

To ensure that differences between different cultural samples are not caused by factors

other than variables of interest, samples in question must possess similar characteristics (Lytle et

al., 1995). The typical criteria for sample similarity are comparability on demographic factors

such as income, education, age (Wallendorf & Reilly, 1983) and key organizational characteristics

(e.g., length of service, job level, etc.). Also, sampling methods should be consistent across cross-

cultural groups (Sin, Cheung and Lee, 1999).

Semantic equivalence

Semantic equivalence refers to measurement constructs of an instrument, its items and

measurement scales serving the same function and being expressed in a similar fashion in different

cultural contexts. Comparing different ethnic groups, a researcher must be sure to the maximum

extent possible that differential instrumentation has been ruled out, in other words, that differences

in scores are �not due to some translation function of the measurement instrument� (McGorry,

2000, p.76). Semantic equivalence is achieved through careful translation of words, phrases and

sentences in a way to preserve a maximum transfer of meaning from one language to another.

This can be a formidable challenge since transfer of meaning is not assured through direct literal

translation of words and phrases. Small et al. (1999) give an example of difficulty in translating

items containing colloquial English phrases (e.g., �things have been getting on top of me�) into

other languages. Small et al. (1999) also give an example of a challenging English-Vietnamese

translation of the following item: �I have been so unhappy that I have had difficulty sleeping�.

Vietnamese translation of the word �unhappy� also contained a connotation of �irritable� which

was not implied in the English language.

5

There are two common translation techniques oriented toward achieving semantic

equivalence: Back Translation Method and Bilingual Participants (Hulin et al, 1983). The Back

Translation method is a multi-step process. The original survey instrument is translated from the

source language into the target language. After that, back translation of the instrument from the

target into the source language is performed by a few independent translators. After that, the

original and the back-translated versions are compared, and discrepancies between them are

identified. By going through a few iterations of this cycle, the proximity of content and meaning

between the different language versions of the instrument is maximized.

Bilingual Participants is a process of completion of both source and target language

versions of the instrument by bilingual individuals. Equivalence of both versions of the

instrument is reached if the different language versions of the instrument items and scales yield

highly similar scores coming from the same individual.

While semantic equivalence is a necessary condition in achieving overall equivalence of

measurement instruments, this condition is not sufficient. With all the rigor and expense that the

traditional survey translation methods require, they have considerable limitations. In the Back

Translation method, the evaluation process is concentrated on the source language, and the issues

of concepts� meaning in the target language are not addressed directly. In the Bilingual

Participants method, the interpretive thinking of bilingual individuals may be considerably

different from the thinking of monolinguals who are the actual focus of the translation efforts

(Hulin et al, 1983). Therefore, translation techniques are not sufficient to ensure that different

language versions of survey items and scales are interpreted in the same fashion by different

cultural groups and, consequently, cannot guarantee meaningful cross-cultural comparisons.

6

A bi-lingual translator may be unsuspecting about a defect in the measurement function of

a seemingly well translated instrument. Ellis (1989) gives an example of a hard to detect English-

German translation flaw in a test item which assessed verbal reasoning of American and German

students. The item had �true�, �false� and �not enough information to determine� response

choices to the following statement: �all of Cindy�s dogs are in the park.� The statement was

preceded by a sequence of logical conditions: �All dogs in the park are retrievers; some of Ann�s

dogs are in the playground; Cindy owns a poodle; most of Ann�s dogs are poodles. All of Cindy�s

dogs are in the park.� Upon discovering that the item elicited markedly different response form

German vs. American students of similar verbal ability, the translation was scrutinized further. It

was discovered that the breed of poodle originated in Germany as �waterfowl retrievers� having

root in the German word �puddeln� which in English means �splash� (Ellis, 1989). The breed was

adopted in France as a decorative pet dog and by the time the breed of poodle reached the United

States, its connection with the word �retriever� was lost. However, in Germany the connection

between the two words remains, and the terms �retriever� and �poodle� could be interpreted

interchangeably, therefore confounding the item logic for the German students.

Conceptual equivalence

As mentioned above, etic research orientation relies on the assumption that measurement

constructs or �phenomena� operationalized in the source language and culture are language-

independent and, provided adequate translation, have the same meaning in different cultural

settings. The question becomes: can it be assumed that individuals from different cultural groups

will use the same definitions and interpretations of measurement concepts expressed in the

language and put those in the same frame of reference? For example, a concept of Persuasiveness

can be viewed in positive terms, such as assertiveness-related, in one culture but can have negative

7

connotations, such as intrusiveness, in another culture. Recent research indicates that fundamental

human mental processes such as perception, categorization and inference are culture-bound

(Nisbett, 2003), therefore throwing a cautionary flag at the assumptions of mental models�

universality. How do we know that we study the same phenomena when placed in different

cultural contexts?

Methods based on factor analysis have been developed over the years addressing the issues

of construct comparability between different groups (Buss & Royce, 1975; Douglas & Craig,

1983). For example, Irvine and Carroll (1980) suggested a set of guidelines within exploratory

factor analysis indicating that measurement constructs are conceptually equivalent across different

groups: (a) there is a same number of factors extracted from the data of each group, (b) there are

the same items loading onto the same factors across groups, (c) there are the same proportions of

total variance accounted for by the same factors across groups, and (d) there is similar pattern of

intercorrelations between the extracted factors in different groups. Such conditions would indicate

so-called invariance of factor structures across groups. Factor structure invariance would confirm

that the same questionnaire items measure the same underlying latent constructs in different

groups, therefore implying that the conceptualization and interpretation of the constructs in

different populations is similar. Similar patterns of factor intercorrelations would suggest that

individuals in different groups put the constructs into the wider contextual frame of reference in a

similar way.

Normative equivalence

Culture may impose influence in the manner in which individuals respond to

questionnaires which may introduce systematic bias to cross-cultural comparison analyses.

Behling and Law (2000) specify a few response tendencies that may systematically color

8

individual expression in characteristic ways, for example: [a] self-enhancement (i.e., favorable

bias in reference to self) vs. modesty (i.e., unfavorable bias in reference to self), [b] conformity

(i.e., responding in socially desirable ways) vs. assertiveness (i.e., expressing one�s opinion

regardless of circumstances) and [c] central tendency (gravitating toward ambiguous point in the

scale) vs. extreme response style (expressing oneself clearly, unambiguously and strongly). Once

it is determined that different cultural groups interpret the measurement constructs of interest in

the similar fashion, the question remains: are their numeric scores comparable? Does a score of

3.5 from, for example, a French individual and a score of 3.5 from a Japanese individual on a

particular measure of satisfaction indicate that these individuals are equally satisfied? Does the

score of 4.0 from a French respondent and 4.5 from a Japanese respondent means the one is

slightly more satisfied than the other?

Different cultural groups may differ in perception of intervals between the response

options or anchors of measurement scales. For some groups it may be difficult to mark the

extreme ends of the sale (e.g., strongly disagree) even if they have a fairly strong opinion on the

matter. Such reluctance can be attributed to such socio-cultural factors as social desirability or

acquiescence (Mullen, 1995). For such individuals, the perception of the interval between

�disagree� and �strongly disagree� may be larger than between �partly disagree� and �disagree�.

Such groups will exhibit central tendency in their response pattern (Lee & Green, 1991). Other

cultural groups, on the other hand, may perceive all the scale intervals as equal.

Hui and Triandis (1989) proposed a mechanism of cognitive mapping of subjective

categories onto the response scales. Subjective categories are a cognitive property of a respondent

(e.g., �this is OK�, �this is Marvelous�). Response scales are provided by a survey researcher

(e.g., �Partly Agree/Partly Disagree�, �Strongly Agree�). The individual subjective categories

9

may match the survey response categories one-to-one, which is an ideal situation. However, if the

number of individual categories and survey scale response options [1] do not match and [2] the

former are not distributed evenly along the spectrum of the later, systematic response differences

may be present between individuals or groups. For example, if individuals have more subjective

categories than allowed by the response scale , the comparative excess of subjective categories

may result in them being �crammed� at the end of the response scale therefore resulting in the

extreme ends of the scale being checked more frequently not because of stronger opinions, but by

the mere fact that the ends of the scale encompass more subjective categories than the middle part

of the scale.

Interpretation of survey scale response options may also differ across languages and

cultural groups. For example, a �neither agree nor disagree� mid-point on a Likert scale may

mean �no opinion on the matter� for one group and �mild agreement� for the other. Languages

may vary in variety and number of words available to form response categories. For example,

Wright et al. (1978) found that, with respect to probabilistic thinking, British respondents had a

larger vocabulary arsenal compared to Asian respondents.

Systematic bias in response may affect factorial structure of the data. For instance, more

(or less) prevalent use of response scale end points could affect the magnitude of correlation

coefficients, therefore affecting the patterns in which factors emerge (Hui & Triandis, 1989). This

could create artificial differences in instrument factorial structures across different groups.

Therefore, factorial invariance among groups is also a validation of the absence of response bias

across groups.

Reliability and Scalar Equivalence. Estimation of whether different groups respond to

measurement scales in the same way could be done through checking error patterns in the data.

10

There are two relevant sources of error patterns in the data: inconsistent scoring between groups

and scalar inequivalence (Mullen, 1995). The first refers to random error due to potential mis-

understanding of scaling methods by different cultural groups. Such error reduces the reliability

of the measures and diminishes the interpretive power of the between-group comparison analyses

(Parameswaran & Yaprak 1987). Therefore, similarity of reliabilities of latent variables across

groups is a necessary condition of cross-cultural comparisons (Parameswaran & Yaprak 1987).

When difference in reliabilities is found between groups, attenuation formulas must be used to

factor out the confounding influence (Singh, 1995). The second, scalar inequivalence or response

set bias, refers to systematic difference of response between groups due to difference in calibration

of the psychological distance between response options of a given ordinal scale caused by such

cultural factors as social desirability, acquiescence or humility (Mullen, 1995). Such bias could

threaten the validity of the measurement scale (Bollen, 1989) when applied in cross-cultural

comparison.

Various approaches have been tried to address the issue of scalar equivalence. Douglas

and Craig (1983) and others suggested multiple methods of measurement. Morris and Pavett

(1992) suggested Profile Analysis: a visual comparison of groups� patterns of construct means.

Perreault and Young (1980) applied Optimal Scaling method to estimation of scalar equivalence.

The Optimal Scaling method rescales nominal or ordinal variables into interval scale variables

while preserving the variable means. If the rank orders and distances between the rescaled values

are the same across cultural groups, scalar equivalence is achieved. Together with conceptual

equivalence, establishing scalar equivalence allows to compare means of observed and latent

variables across different groups (Jöreskog and Sörbom, 1989).

11

Using Structural Equation Modeling in Establishing Measurement Equivalence

Some researchers unite the variety of equivalence test types (i.e., conceptual, scalar) under

one rubric: measurement equivalence (Riordan & Vandenberg, 1994). The variety of tests

required to establish measurement equivalence can be administered simultaneously using

Structural Equation Modeling (SEM) approach (Byrne, 1994). Applications of SEM to the

assessment of measurement invariance were pioneered by a Swedish statistician Karl Jöreskog in

the 1970�s (Jöreskog, 1971). Jöreskog and Sörbom (1989) developed a system of interrelated

factor-analytical and chi-square tests allowing establishing measurement equivalence across

different samples; the procedure was labeled Multigroup Analysis or Multisample Analysis.

Similar to confirmatory factor analysis, SEM tests the relationships between observable

variables (e.g., questionnaire items) and latent variables (e.g., questionnaire dimensions) in so-

called measurement model. Latent variables are useful in explaining covariation among observed

variables (Dunn et al., 1993). Utilization of latent variables also allows reducing the overall effect

of measurement error of any single observed variable, therefore improving accuracy of the results

(Kline, 1998).

The following example considers two latent variables: Organizational Commitment (OC)

and Job Satisfaction (JS). The first latent variable (F1) is measured by three items: belief in

organization�s bright future (v1), being proud to work for the organization (v2) and favorably

comparing the organization with other places to work (v3). The second latent variable (F2) is also

measured by three items: satisfaction with the type of work one does (v4), ability to grow

professionally in one�s job (v5) and being appropriately recognized for one�s work (v6). A series of

equations specifies relationships between the observed and latent variables, as shown in Table 1.

12

Table 1.

System of Equation Specifying Model of OC and JS

Organizational Commitment Job Satisfaction

Measure 1 V1 = λ11F1 + E1 V4 = λ42F2 + E4



The lambda (λk) coefficients represent the magnitude of the expected change in the

observed variable per a unit of change in the latent variable; they are regression coefficients for the

effects of latent variables on the observed variables. The epsilon (Em) variables are the errors of

measurement for the observed variables. For each observed variable, a measurement error term

represents the unexplained by the latent construct variance in the observed measure. In the context

of factor analysis, an error of measurement consists of two components: systematic variance and

random variance associated with each observed variable (Bollen 1989; Mullen, 1995).

To establish measurement invariance across different groups, Jöreskog and Sörbom (1989)

suggested a progressively restrictive set of tests enabling the following answers: [1] whether the

factorial structures (specific items loading onto specific factors and also factor correlation

patterns) are significantly similar across different samples which would confirm comparability of

measurement constructs; and [2] whether the error variance/covariance patterns are significantly

similar across the samples. Since the error terms include both systematic and random error

components, establishing similarity in error patterns between different groups would address the

issue of scalar equivalence, or calibration of true scores, and the issue of inconsistent scoring of

the scales across the samples (Mullen, 1995).

13

SEM: Process Steps

Structural Equation Modeling (SEM) tests the viability of relationships between variables,

either observed or abstract. Using confirmatory factor-analysis (CFA) and chi-square statistics,

SEM tests how closely the relationships specified in the theoretical model reflect the actual

relationship patterns in the data. In other words, the SEM approach estimates how closely the

imposed model fits the data. The fundamental requirement for the existence of an estimate

solution for a model is its so-called identification status.

Model Specification and Identification

The system of relationships between variables specified in the model can also be expressed

in a pictorial format. Using the previous example, the model in Figure 1 depicts two constructs:

organizational commitment and job satisfaction. Each construct is measured by three items.

Figure 1. Relationship model of OC and JS.

OrganizationalCommitment

V01

V02

V03

V11

V12

V13

E01

E02

E03

E11

E12

E13

Job Satisfaction

Represented by the single-headed arrows between latent and observed variables are

regression coefficients of observed variables onto latent variables. For each observed variable, a

measurement error term represents the unexplained by the latent construct variance in the

observed measure. Links between latent and observed variables and error terms comprise the so-

called measurement model (or CFA model) (Byrne, 1994). Represented by the double-headed

arrows are covariances of latent variables, which constitute the so-called structural model (Byrne,

14

1994) and specify the patterns of relationships between abstract constructs (e.g., job satisfaction

and commitment to organization) that the researcher hypothesizes to be true.

Again, presented in a mathematical form, a model in Figure 1 can be represented as a

system of equations encompassing measurement model as reflected in Table 2.

Table 2.

System of Equation Specifying Model of OC and JS





To incorporate information contained in the raw data (i.e., multiple collected values of

each observed variable), equations in Table 2 could be rewritten in a succinct format which

utilizes each variable�s variation from its mean. Under the assumption that error terms are not

correlated with any other variables in the model (Dunn et al., 1993), the system of equations can

be presented in the form of variances as reflected in Table 3.

Table 3.

Succinct System of Equation Specifying Model of OC and JS.


Measure 1 Var(V1)=λ112Var(F1)+Var(E1) Var(V11)=λ42

2Var(F2)+Var(E4)


2Var(F2)+Var(E5)


2Var(F2)+Var(E6)

15

In order to draw inferences and conclusions about a model, the system of equations must

be solved for each of its unknown parameters. Variances and covariances of the observed

variables are so-called known parameters and can be obtained from the raw data (e.g., Cov(V1,V2)

= Σ(V1-V1)(V2-V2)/(N-1)). The rest of the parameters in the model (i.e., variances of the latent

variables and error terms as well as regression coefficients) have to be estimated through the

already identified known parameters (also called data points). A solution is possible if the number

of parameters to be estimated is no larger than the number of known data points (i.e., variances

and covariances of the observed variables). Using the previous example, the system of equations

in Table 3 yields 21 known statistics: Var(V1), Var(V2), Var(V3), Var(V4), Var(V5), Var(V6),

Cov(V1, V2),. Cov(V1, V3), Cov(V2, V3), Cov(V4, V5), Cov(V4, V6), Cov(V5, V6), Cov(V1, V4),

Cov(V1, V5), Cov(V1, V6), Cov(V2, V4), Cov(V2, V5), Cov(V2, V6), Cov(V3, V4), Cov(V3, V5),

Cov(V3, V6), and 15 unknown parameters to be estimated: λ11, λ21, λ31, λ42, λ52, λ62, Var(F1),

Var(F2), Cov(F1, F2), Var(E1), Var(E2), Var(E3), Var(E4), Var(E5) and Var(E6).

In case the number of estimated parameters exceeds the number of known data points, the

model in question contains insufficient information and yields an infinite number of solutions. If,

however, the number of parameters to be estimated equals the number of known data points, the

model is called just-identified and yields a single solution. Finally, containing more data

variances/covariances than unknown structural parameters, like in the previous example, a model

is called over-identified. This type of model implies certain equality conditions among known

population parameters and is of primary use to scientific research as will be described below.

Since the just-identified type of model yields a single perfect solution, it is considered

scientifically uninteresting - there is simply nothing to test and prove (Byrne, 1994). The over-

identified model type is of primary use to scientific research because, containing a number of

16

degrees of freedom, it allows for the possibility of rejection of the model, therefore enabling

hypothesis testing (Byrne, 1994), which will be illustrated later in the paper.

Assessment of Overall Model Fit to the Data

As mentioned before, SEM tests how closely the relationships specified in the model by

the researcher reflect the actual relationship patterns in the data (i.e., the data variance/covariance

patterns presented in the form of a covariance matrix). Based on the information contained in the

data, provided that the model is identified, SEM derives the estimates for the full set of parameters

reflecting relationships in the specified model. A fundamental hypothesis of model specification

is that the covariance matrix of the observed variables is accurately expressed as a function of the

set of derived model parameters (Bollen, 1989). To illustrate this point, the model represented by

a system of equations in Table 2 can be presented in a matrix form.

Figure 2. Matrix form of equation system specifying model of OC and JS.

V1 λ11 0 E1

V2 λ21 0 E2

V3 λ31 0 F1 E3

V4 0 λ42 F2 E4

V5 0 λ52 E5

V6 0 λ62 E6

= * +

Expressed in a reduced form, the system of equations in Figure 2 can be represented as

VN = ΛNFI + EN, where N = 6, I = 2 and Cov(FI, EN) = 0 and E(EN) = 0 for all I and N. It can be

shown that covariance matrix VNN of N observed variables and q observations can be derived from

a product of the matrix VNq of raw data points expressed as deviations from variable means and its

transposed representation VNq� (Bollen, 1989, pp. 455-456): Cov(VNN) = (N-1)-1 * (VNq* VNq�) or,

in a simplified form, Cov(VN) = (N-1)-1*(VN*VN�) = E(VN*VN�), where E() refers to as �expected

value of� or �mean of.� It can also be shown that E(VN*VN�) can be expressed as a function of

17

model parameters: E(VN*VN�)=E[(ΛNFI + EN)*(ΛNFI + EN)�] = E[(ΛNFI + EN)*(FI�ΛN� + EN�)] =

ΛN*E(FIFI�)*ΛN� + E(EN*EN�) = ΛNΦΛN� + ΘE, where Φ is a covariance matrix of latent variables

FI and ΘE is a covariance matrix of error terms EN (Bollen, 1989, pp.85-86).

ΛN*E(FIFI�)*ΛN� + E(EN*EN�) = ΛNΦΛN� + ΘE, where Φ is a covariance matrix of latent variables

FI and ΘE is a covariance matrix of error terms EN (Bollen, 1989, pp.85-86).

Applied to the model of organizational commitment and job satisfaction represented in

Figure 1, the covariance matrix of latent variables and the covariance matrix or error terms can be

expressed as presented in Figure 3.

Applied to the model of organizational commitment and job satisfaction represented in

Figure 1, the covariance matrix of latent variables and the covariance matrix or error terms can be

expressed as presented in Figure 3.

Figure 3.Figure 3. Covariance matrix of observed variables and residual terms in the model of OC and JS.

var (F1) φ11cov (F1,F2) var (F2) φ21 φ22

=Φ model =

var (E1) 0 0 0 0 00 var (E2) 0 0 0 00 0 var (E3) 0 0 00 0 0 var (E4) 0 00 0 0 0 var (E5) 00 0 0 0 0 var (E6)

ΘE model =

Substituting matrices Φmodel and ΘE model into the equation Cov(VN) = ΛNΦΛN� + ΘE:, the

unknown elements of the model can be expressed through the variances and covariances of the

model�s observed variables as shown in Figure 4.

Figure 4. Model unknown parameters expressed through the known population parameters.

var(v11) λ211φ11 + var(ε1)

cov (v11,v21) var(v21) λ21λ11φ11 λ221φ11 + var(ε2)

cov (v11,v31) cov (v21,v31) var(v31) λ31λ11φ11 λ31λ21φ11 λ231φ11 + var(ε3)

cov (v11,v42) cov (v21,v42) cov (v31,v42) var(v42) λ42λ11φ21 λ42λ21φ21 λ42λ31φ21 λ242φ22 + var(ε4)

cov (v11,v52) cov (v21,v52) cov (v31,v52) cov (v42,v52) var(v52) λ52λ11φ21 λ52λ21φ21 λ42λ31φ21 λ52λ42φ22 λ252φ22 + var(ε5)

cov (v11,v62) cov (v21,v62) cov (v31,v62) cov (v42,v62) cov (v52,v62) var(v62) λ62λ11φ21 λ62λ21φ21 λ62λ31φ21 λ62λ42φ22 λ62λ52φ22 λ262φ22 + var(ε6)

=

18

From the matrix-form representation in Figure 4 it can be seen that the known

variance/covariance values of the observed variables can be expressed as a function of unknown

model parameters, as reflected in Table 4.

Table 4.

Population Known Parameters Expressed as a Function of Model Unknown Parameters

Equation # Model Status

1 var(v11) = λ211φ11 + var(ε1) 1 var(v11) 3 λ11, φ11, var(ε1) Underidentified

2 var(v21) = λ221φ11 + var(ε2) 2 var(v11), var(v21) 5 λ11, φ11, var(ε1), λ21, var(ε2) Underidentified

3 cov (v11,v21) = λ21λ11φ11 3 var(v11), var(v21), cov (v11,v21) 5 λ11, φ11, var(ε1), λ21, var(ε2) Underidentified4 var(v31) = λ2

31φ11 + var(ε3) 4 etc. � 7 etc. � Underidentified5 cov (v21,v21) = λ21λ31φ11 5 7 Underidentified6 cov (v11,v31) = λ31λ11φ11 6 7 Underidentified7 var(v42) = λ2

42φ22 + var(ε4) 7 10 Underidentified8 cov (v31,v42) = λ42λ31φ21 8 10 Underidentified9 cov (v21,v42) = λ42λ21φ21 9 10 Underidentified

10 cov (v11,v42) = λ42λ11φ21 10 10 Identified11 var(v52) = λ2

52φ22 + var(ε5) 11 13 Underidentified12 cov (v42,v52) = λ52λ42φ22 12 13 Underidentified13 cov (v31,v52) = λ42λ31φ21 13 13 Identified14 cov (v21,v52) = λ52λ21φ21 14 13 Overidentified15 cov (v11,v52) = λ52λ11φ21 15 13 Overidentified16 var(v62) = λ2

62φ22 + var(ε6) 16 15 Overidentified17 cov (v52,v62) = λ62λ52φ22 17 15 Overidentified18 cov (v42,v62) = λ62λ42φ22 18 15 Overidentified19 cov (v31,v62) = λ62λ31φ21 19 15 Overidentified20 cov (v21,v62) = λ62λ21φ21 20 15 Overidentified21 cov (v11,v62) = λ62λ11φ21 21 15 Overidentified

Knowns UnknownsEquations

Equations relating population and model parameters are restrictions placed on a theoretical

model as it is being specified. The sequence of equations in Table 4 can be looked at as a gradual

�build up� of the model or as a sequence of progressively complex models. For example, with

equation 1 only, a model would consist of one latent variable F1, one observed variable V1 and an

error term E1. Specified further with equations 1 through 7 only, a model would consist of one

latent variable and three observed variables and three error terms. Finally, a model represented by

equations 1 through 21 consists of two latent variables which are specified to covary and each

being defined by three observed variables with corresponding error terms as represented in Fig. 1.

From the gradual build up of the model it can be seen in Table 4 how the proportional

representation of cumulative known and unknown elements in the system of equations changes as

19

the number of equations increases from 1 to 21. As mentioned earlier, a necessary condition for

an equation system to have a unique solution is equality in number of its known and unknown

elements. This condition is met when the model is specified by restrictions expressed in equations

1 through 10, at which point the model becomes identified (or just-identified). A graphic

representation of the model specified by equations 1�10 is shown in Figure 5.

Figure 5. Just-identified model of OC and JS.


V1V2V3

V4E1E2E3

E4Job Satisfaction

A basic and necessary rule of model identification is the so-called t-rule (Bollen, 1989). In

a model with p number of observed variables, the number of variances and covariances is p(p

+1)/2, putting a limit on the number of unknown parameters that can be estimated in the model.

In practical terms, achieving model identification is a frequent problem (Byrne, 1994). For

example, a model specified by equations 1-6 in Table 4 has six known and seven unknown

elements and is under-identified. The pictorial representation of this model is shown in Figure 6.

Figure 6. Under-identified model of OC.


V1V2V3

E1E2E3

There are additional parameter restriction requirements, however, necessary for practical

application of any model. Since latent variables in the model (e.g., organizational commitment)

cannot be observed directly, they do not have a measurement scale. In order to be interpretable, a

latent variable can be �scaled� to one of the observed variables by setting the regression

20

coefficient of that observed variable onto the latent variable to one (Bollen, 1989). Therefore, a

change in one unit of the observed variable corresponds exactly with a change in a unit of a latent

variable.

Applying the �scaling� requirement to the model in Figure 6 and fixing one of the factor

loading to one (e.g., λ11=1), the model becomes just-identified and could be expressed trough the

system of equations in Table 5.

Table 5.

Population Parameters Expressed as a Function of OC Model Parameters


1 var(v11) = φ11 + var(ε1) 1 var(v11) 2 φ11, var(ε1) Underidentified2 var(v21) = λ2

21φ11 + var(ε2) 2 var(v11), var(v21) 4 φ11, var(ε1), λ21, var(ε2) Underidentified3 cov (v11,v21) = λ21φ11 3 var(v11), var(v21), cov (v11,v21) 4 φ11, var(ε1), λ21, var(ε2) Underidentified4 var(v31) = λ2

31φ11 + var(ε3) 4 etc. � 6 etc. � Underidentified5 cov (v21,v31) = λ21λ31φ11 5 6 Underidentified6 cov (v11,v31) = λ31φ11 6 6 Identified

Equations Knowns Unknowns

Unknown parameters of the just-identified model in Table 5 can be uniquely expressed

through variances and covariance of the observed variables as shown in Table 6. A set of

parameters in a just-identified model would produce the matrix identical to that of the observed

variables.

Table 6.

OC Model Parameters Expressed Through Population Parameters

cov (v21,v31)cov (v11,v31)

cov (v21,v31)cov (v11,v21)

cov (v11,v21) cov (v11,v31)cov (v21,v31)




=λ31

=λ21

=φ11

var(ε3) = var(v31) -

var(v11) -

var(v21) -

var(ε1)

var(ε2) =

=

21

Since the model parameters are unknown and are estimated, the covariance matrix

produced with these parameters is called �implied�. If a model is not just-identified (i.e., over-

identified), SEM derives a set of competing solutions. The goal of SEM for over-identified

models is to find the �best� solution for the set of model parameters that results in minimum

possible differences between the implied matrix Σ(θ) (where θ is a vector of model parameters)

and the sample covariance matrix S (which represents the population of interest) (Bollen, 1989).

In other words, the residual matrix S - Σ(θ) needs to be as close to zero as possible.

To achieve the minimum possible S - Σ(θ) difference solution, SEM employs fitting

functions. There are a series of fitting functions used in SEM that underlie the derivation of model

parameters, for example, Maximum Likelihood Estimation, Unweighted Least Squares and

Generalized Least Squares (Bollen, 1989). The Maximum Likelihood Estimation is the most

common method for parameter estimation (Bollen, 1989; Kline, 1998) and is used in this study.

However, the following example uses Unweighted Least Squares (ULS) function to illustrate the

process of estimation of model parameters due to relative ease of presentation of ULS.

To illustrate the estimation process using ULS, a simplest form of a model is used

specified by a single equation and containing observed variables only: y = x + e, where y is

dependent variable, x is independent variable and e is an error term containing unexplained

variance in y by x. The variables� sample covariance matrix and matrix of model parameters can

be expressed as presented in Figure 7.

Figure 7. Covariance matrix of observed variables and parameter matrix of the �example� model.

cov (x,y) var (x)var (y) cov (y,x)S = φ + ψ φΣ(θ) =

φ φ

22

Phi (φ) is implied variance of the independent variable and psi (ψ) is implied variance of

the error term. The residual matrix S - Σ(θ) in this case can be presented as:

[S - Σ(θ)] = [(var(y) � φ - ψ) + 2(cov(y,x) - φ)) + (var(x) - φ)].

The Unweighted Least Squares is a function of squared deviations between the observed

elements of S and predicted elements of Σ(θ): FULS = 0.5*tr[(S- Σ(θ))2]. Substituting the previous

equation: FULS = 0.5*[(var(y) � φ - ψ)2 + 2(cov(y,x) - φ)2) + (var(x) - φ)2].

A necessary condition for minimization of a function is setting its first-order partial

derivatives to zero and solving for the unknown parameters (Anton, 1998), as reflected in Fig. 8.

Figure 8. Derivation of parameters providing the minimum of the model fitting function.

Since the second partial derivatives of FULS are positive, the values of the model

parameters determined by the equations in Figure 8 identify the minimum point of FULS (Anton,

1998).

An important aspect of parameter estimator functions (e.g., FULS) is that they provide

significance testing of the overall model fit for over-identified models (Bollen, 1989). To

illustrate this point, the following example of the over-identified model in Figure 6 is considered.

23

Table 7.

Population Parameters as a Function of Parameters of the OC Model


1 var(v11) = φ11 + var(ε1) 1 var(v11) 2 φ11, var(ε1) Underidentified2 var(v21) = φ11 + var(ε2) 2 var(v11), var(v21) 3 φ11, var(ε1), var(ε2) Underidentified3 cov (v11,v21) = φ11 3 var(v11), var(v21), cov (v11,v21) 3 φ11, var(ε1), var(ε2) Identified4 var(v31) = φ11 + var(ε3) 4 etc. � 3 φ11, var(ε1), var(ε2) Overidentified5 cov (v21,v31) = φ11 5 3 φ11, var(ε1), var(ε2) Overidentified6 cov (v11,v31) = φ11 6 3 φ11, var(ε1), var(ε2) Overidentified

Equations Knowns Unknowns

If the number of the unknown parameters in the model in Figure 6 is reduced upon the

assumption that λ11 = λ21 = λ31 = 1, the model becomes over-identified, as shown in Table 7, and

φ11 could be expressed as φ11 = cov(V11,V21) = cov(V21,V31) = cov(V11,V31). If the equality of

multiple solutions for an over-identified parameter (i.e., φ11) were true in the population, the

model could be proven correct (i.e., S - Σ(θ) = 0) (Bollen, 1989).

The proprieties of parameter estimator functions (e.g., FULS) are such that (N-1)FULS is a

chi-square distribution with 0.5p(p + 1) - t degrees of freedom, where p is the number of observed

variables and t is the number of free model parameters (Bollen, 1989). Using parameter estimator

functions, SEM goes through an iterative process of deriving set of parameters for a model. For

just-identified models, the ultimate set of predicted covariances would eventually equal the

observed ones: the difference between implied and sample variance/covariance matrices in just-

identified models is zero and so are the number of degrees of freedom and the chi-square statistics.

The derivation process is called to �converge� on a solution. For over-identified models, however,

only approximation between the predicted and the observed covariances can be achieved (Kline,

1998). With a positive number of degrees of freedom, the chi-square statistics for an over-

identified model constitutes a test of significance of the difference in model fit to the data between

the over-identified model and a hypothetical just-identified version of it - a model with a residual

matrix equal to zero. A non-significant chi-square would indicate that overall fit of the over-

24

identified model is no worse than the �perfect� fit of the just-identified version of the model.

Therefore, low and non-significant χ2 values are desirable and constitute a good fit of a model to

the data. A good fit is the one that is not significantly different from the �perfect� fit confirms the

validity of the model or, in other words, the equality of multiple solutions for over-identified

parameters holding true in the population (Bollen, 1989).

A strong limitation of χ2 statistic as a goodness of fit index is its sensitivity to sample size.

Taken a large sample size, the value of χ2 can be found significant even if there are only slight

differences between model-implied and the sample-observed covariance matrices (Kline, 1998).

To overcome this limitation, various alternative overall model fit indices have been developed.

For example, Bentler (1990) developed a Normed Fit Index (NFI). NFI = (χ02- χk

2)/χ02, where χ0

2

is the statistic for the so-called null model and χk2 the statistic for the hypothesized model.

According to Bentler (1990), the null model is a special case of the hypothesized model with all

the relations between variables set to zero. This model provides an initial test of whether there is a

covariance structure to be explored in the first place. In case the null model provides a good fit to

the data, there is little sense in further testing.

The null model can serve as a good base model against which to compare successive

models. Naturally, χ02 is expected to be high and significant. The purpose of the hypothesized

model is to specify relationships between variables with the goal to achieve significant

improvement in model fit, as reflected in the NFIndex. The NFI range is 0 to 1. The value higher

than .90 indicates acceptable fit of the hypothesized model to the data (Bentler, 1990).

The limitation of the NFI, however, is its tendency to underestimate fit in cases of small

samples. To address this problem, a more robust Comparative Fit Index (CFI) was developed

25

(Bentler,1990): CFI = [(χ02 - df0) - (χk

2 - dfk)]/(χ02 - df0). CFI is a widely utilized measure among

SEM users (Byrne, 1994). CFI will be used as a primary measure in the current study.

Assessment of Specific Model Parameters as Related to Overall Model Fit

Given a particular achieved model fit, a reasonable practical goal is to attain an

improvement in fit to the data. This can be done through adding or deleting parameters of the

model (Bentler, 1995). Adding parameters (referred to as �model building�) is analogous to

putting non-zero specifications on parameters, which were previously fixed to zero (Kline, 1998),

therefore increasing comprehensiveness and complexity of the model. Conversely, deleting

parameters (referred to as �model trimming�) is analogous to fixing non-zero parameters to zero

thereby eliminating paths and simplifying the model (Kline, 1998). The difference in χ2 statistics

between the original model fit and the modified model fit to the data is also a χ2 distribution

(∆χ2 = χ2orig - χ2

modif, df = dforig - dfmodif) allowing testing for significance in change of fit. A set of

gradually modified models with the purpose of achieving better fit to the data is referred to as

hierarchical or nested models.

An insight into which specific parameters must be added or eliminated to achieve a �better

model� comes from χ2 tests commonly used in SEM software: Lagrange Multiplier and Wald Test.

Lagrange Multiplier test (LM) indicates the amount by which the overall model fit would increase

if a particular parameter not currently included in the model was specified to be freely estimated

(i.e., included in the system of model equations as a variable and not a constant). In its turn, Wald

statistic (W) estimates the overall decrease in model fit if a particular free parameter was fixed to

zero (or eliminated from the model). A non-significant value of W statistic would identify

redundant model parameters not contributing to the overall model fit.

26

Multigroup Analysis

As mentioned previously, equality in observed-to-latent variable relationships across

different groups as well as similarity of latent variables� covariance patterns and similarity of error

variances/covariances across groups is required to establish measurement equivalence of

instruments used by those groups.

The multigroup analysis technique first assesses the fit of the combined model, which

incorporates models from each group. Following, a series of equality constraints is gradually

imposed between the groups� models in a nested model fashion: first equality of factor loadings is

imposed, then equality of factor covariances and, finally, equality of error variances/covariances is

imposed (Byrne, 1994). In case the combined model provides an adequate fit to the data at each

step of the nested models� process, measurement equivalence between the considered groups is

established (Byrne, 1994).

Assumptions for Analyzed Data

Most of the widely used SEM parameter estimation procedures rely on certain

characteristics of the data under analyses: low volume of missing observations, not-too-high level

of intercorrelation among variables, univariate and multivariate normality of variable distributions,

and absence of univariate and multivariate outliers in the data (Kline, 1998).

The two traditional ways of dealing with missing observations are [1] substituting missing

data with estimates based on patterns in the available data and [2] excluding cases with missing

observations form analyses (Cohen and Cohen, 1983). Substitution of missing data for a

particular participant can be accomplished through a variety of methods (Raymond & Roberts,

1987). Substituting missing values with a sample average for the variable is the simplest

approach. However, this approach does not take into account participant�s individual pattern of

27

response. Replacing a missing score with a predicted score derived by multiple regression based

on a particular respondent�s non-missing data is sensitive to the individual pattern of response;

however, this approach is not appropriate if the missing observation variable does not covary with

the rest of the variables. Another substitution alternative is replacing a missing observation with

an observation by another respondent with a close match in response profile across all variables.

This method is available in the PRELIS programs associated with LISREL (Jöreskog & Sörbom,

1996).

The two methods of excluding missing observations from analyses are listwise deletion

and pairwise deletion. With listwise deletion, cases with missing observations on any variable are

excluded from all analysis computations. Compared to other approaches, the limitation of this

method is that it leads to the largest reduction in effective sample size. Different from listwise

deletion, pairwise deletion excludes cases on computation-by-computation basis: cases are deleted

from a computation only if they have missing observations on variables involved in that particular

computation. Using pairwise deletion method in SEM may cause values in covariance matrix to

be based on different number of cases potentially making the matrix unsuitable for certain matrix

algebra operations (e.g., inversion). Such matrix is called nonpositive definite or singular and one

of its characteristics is zero or negative determinant (Kline, 1998).

Very high levels of correlation (.85 or higher) among variables (multicollinearity) suggests

their redundancy in that one can be substituted for another in explaining phenomena under study.

Although, a certain degree of variable intercorrelation is necessary to justify creation of latent

measurement constructs, too-high level of correlation among variables is problematic for two

reasons. First, removal of a redundant variable should not affect validity and reliability proprieties

of a particular latent construct. Therefore, according to the rule of parsimony, presence of

28

redundant variables is not beneficial. Second, multicollinearity can cause the variable covariance

matrix to be nonpositive definite causing SEM estimation procedures to fail (Kline, 1998).

Bivariate multicollinearity can be easily detected by examining the correlation matrix.

Multicollinearity on the multivariate level can be computed by calculating squared multiple

correlations coefficient (R2) between each variable and the remaining variables. Coefficients

greater than .90 suggest multicollinearity. Variables that are identified to contribute to

multicollinearity can be removed from the analysis as they carry redundant information.

The standard SEM fitting functions used in derivation of model parameters (e.g., FULS)

possess several important characteristics (Bollen, 1998). For instance, matrix of estimated model

parameter values θ is asymptotically consistent meaning that as sample size grows larger θ

converges onto the matrix of population parameter values θ. Also, (N-1)FULS have asymptotic chi-

square distribution allowing significance testing for the overall model fit. These assumptions hold

true for observed variables with either multinormal distribution or distribution without excess

kurtosis (Bollen, 1998). If multinormality assumption is violated, alternative fitting functions

assuming non-normal distribution (e.g., elliptical distribution, arbitrary distribution) can be used.

Alternatively, correction for non-normality can be used. For example, Saratoga-Bentler Scaled

Statistic �incorporates a scaling correction for the χ2 statistic when distributional assumptions are

violated. � [It] has been shown to be the most reliable test statistic for evaluating covariance

structures under various distributions.� (Byrne, 1994, p.86).

Outliers, which are cases with extreme values of one variable (more than three standard

deviations from the mean) or with an unusual value pattern across all variables, can cause variable

distributions to be non-normal, therefore violating the normality assumption of the parameter

derision functions used in SEM. Univariate outliers can be detected through examining single

29

variables� z-score distributions. Bivariate outliers can be identified through examining case

deviation from the regression line. There are also techniques for detecting multivariate outliers

(Bollen, 1989). For example, a statistic called Mahalanobis distance estimates the multivariate

distance between a given case and sample means (called �centroids�). This statistic can be

computed in some statistical software packages (e.g., Regression Module of SPSS) and are

interpretable as χ2 statistics with degrees of freedom equal number of variables (Kline, 1998).

In general, outliers may or may not have an effect on covariances among variables and,

therefore, on the model parameter estimates. To determine whether there is such effect, parameter

estimates drawn from the data containing outliers need to be compared to those derived from data

where outliers are removed (Bollen, 1989).

Using Item Response Theory in Establishing Measurement Equivalence

An alternative methodology for estimating measurement equivalence is applications of

Item Response Theory (IRT) (Lytle et al., 1995). IRT has been applied to validating measurement

equivalence of translated employee attitude measurement instruments (Hulin, Drasgow &

Komocar, 1982; Hulin & Mayer, 1986). IRT specifies relationship between an observable

measure (e.g. a survey item) and its underlying construct (e.g., a survey dimension) (Hulin et al.,

1983). The measure-construct relationship can be expressed through a function in Figure 9, where

θ represents an abstract characteristic underlying the measure (e.g., individualism-collectivism

orientation). The type of function in Figure 9 is thought of as representing item�s defining

characteristics (Hulin et al., 1983).

Figure 9. A logistic function of the item characteristic curve.

11 + e

Pi(θ) = - Dai(θ - bi)

30

The function in Figure 9 represents a family of S-shaped ogive curves called item

characteristic curves (ICC) (Hulin, 1983). A graphic example of IIC is presented in Figure 10.

Figure 10. Item characteristic curve.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

θ

P(θ)

a

b

The IC curve represents the probability of an individual endorsing an item as related to

his/her stance on the latent construct underlying the item. As individual�s attitudinal stance on a

particular construct approaches the lower extreme or characteristic�s �negative� pole, the

probability of endorsing an item of the construct approaches zero. Conversely, as the attitudinal

stance on a particular construct approaches the higher extreme, the probability of endorsing an

item of the construct approaches 100%. The curve in Figure 10 is defined by two parameters: �a�

� the amount of θ corresponding with the point of the curve inflection, �b� � the slope of ICC at

the point of inflection. The parameter �a� represents a relative zero of a construct (e.g.

�individualism-collectivism� construct) where there is an equal chance for an individual to

endorse or reject the item. Parameter �b� represents discriminatory power of the item determining

whether an individual falls into the relative �negative� or �positive� part of the construct (e.g.,

�individualism� vs. �collectivism�).

31

Within IRT framework, a translated item is considered metrically equivalent if two

individuals speaking different languages but having the same attitudinal stance on a construct

endorse the item with the same amount of probability. In other words, the IC curves of the

equivalent items should be the same. A lack of metric equivalence between items constitutes so-

called differential item functioning (DIF) (Hui & Triandis, 1983).

A variety of methods testing DIF for significance have been developed (Behling & Law,

2000). For example, the �model comparison method� estimates parameters �a� and �b� in a two-

parameter model combining data from all groups of interest. Similar to the test of factorial

invariance in confirmatory factor analysis, a relational model based on observed variables�

parameter variance-covariance patterns is constructed. The model combined data from all groups

of interest and a measure of model fit to the data is estimated through a χ2 test (Behling & Law,

2000). To estimate metric equivalence of a particular item, the item�s parameters �a� and �b� are

set to differ while the parameters of all other items in the construct are fixed as equal. If the

adjusted model fit to the data does not differ significantly from that of the initial model, the item is

considered metrically equivalent across the groups in consideration.

The relative disadvantage of IRT methods before SEM methodology is that IRT requires

each item�s equivalence to be estimated separately, while SEM allows for testing all items at once

(Lytle et al., 1995). Subsequently, compared to SEM, the IRT method requires more measures for

each construct since each construct is tested separately (Lytle et al., 1995). In addition, the IRT

method is limited to the testing of the measurement model of relating observed and latent

variables. Testing the relationships between latent constructs in a structural model is beyond the

scope of IRT.

32

Literature Overview Conclusion

Cross-cultural research, although still very limited in volume, has been developing through

the last quarter of the 20th century. In the field of international human resource management

research (HRM), 3.6% of the literature concerned international studies in the 1970�s (Adler,

1983), 2.3% in the early 1980�s (Adler & Bartholomew, 1992) and 6% in the late 1980�s (Peng, et

al, 1990). Clark, Grant & Heijltjes� (2000) review of HRM research between 1977 and 1997

shows an increase in the rate of publications in the 1990�s with a bulk of research being published

after 1993. Review of the trends in cross-cultural consumer research indicates that although prior

to 1990�s the research in this area was very limited, it has been receiving increasing attention in

the recent years. Sin, Cheung & Lee�s (1999) review of 19 pertinent marketing and consumer

research journals between 1991 and 1996 shows that international studies comprised 0.56% of

publications in 1991, 0.28% in 1992, 0.82% in 1993, 1.07% in 1994, 0.61% in 1995 and 1.34% in

1996.

Despite the trend of increasing attention paid to cross-cultural research in the 1990�s, the

use of techniques that establish equivalence of measures between samples from different cultures

remains limited. For example, Sin, Gordin and Lee�s (1999) review of published cross-cultural

consumer research between 1991 and 1996 showed that 56% of studies reported translation

equivalence (back translation), 34% reported sample equivalence and only 11% of studies reported

testing for measurement equivalence. The goal of the current study is to contribute to the

increasing number of cross-cultural and cross-language validations of measurement instruments.

33

The Current Study

From the point of view of cultural determinism, certain concepts and associated

measurement constructs (e.g., satisfaction with ones job, commitment to one�s organization) are

assumed to be universal. They are defined and interpreted in similar ways in many different

cultures. However, there are concerns whether knowledge and meaning developed in one culture

can be transferred and applied in another culture (Hofstede, 1980). Therefore, the universality of

measures must be verified in every culture they are used.

In the current study, a covariance structure (SEM) approach was used to assess two forms

of measurement equivalence (conceptual equivalence and scalar equivalence) for several latent

work-related constructs between two culturally diverse groups. The groups represented in the

study are Spanish-speaking and English-speaking employees of a hospitality industry company in

the South Western United States. Both groups were subjects of an organizational survey research

study conducted in 1999.

Establishment of measurement equivalence serves only as a means to meaningful cross-

cultural comparison. The question in focus is how do different cultural groups differ on

psychological measurement constructs of interest. Research indicates that ethnical and cultural

differences can influence work-related attitudes in individuals. For example, a recent study by

Watson Wyatt Worldwide showed that in the United States Hispanics are more positive about

their jobs than other American workers (Reinemer, 1995). Similarly, Lankau & Scandura (1996)

found that Hispanic nurses reported higher job satisfaction compared to White and Black nurses.

Since the published literature concerning the work attitudes of Hispanics is far from being

extensive (Weaver, 2000), only a limited number of constructs can be hypothetically compared in

an a priori fashion.

34

Hypothesis 1

Since the survey used in the current study went through a structured translation process,

and since the original study assumed that the two language versions of the survey were equivalent,

it was hypothesized that both language versions of the survey would elicit the same frames of

reference from the English-speaking and Spanish-speaking samples demonstrating conceptual and

scalar equivalence across the two samples for the following constructs or survey dimensions:

Communications, Leadership, Supervision, Job Content & Satisfaction, and Company Image &

Commitment.

Hypothesis 1a. Data collected by the two language versions of the survey would exhibit

invariance of factor structures of all survey dimensions: similar patterns of item loading onto

factors and similar patterns of factor cross-correlations. A model incorporating both English and

Spanish groups with item loadings and factor cross-correlations set as equal between the groups

would exhibit good fit to the data as indicated by the value of 0.9 or higher of the Comparative Fit

Index.

Hypothesis 1b. Data collected by the two language versions of the survey will exhibit

similar random and systematic error patterns: error variance/covariance patterns would not be

significantly different across the Spanish-speaking and English-speaking samples. A model

incorporating both English and Spanish groups with item loadings, factor cross-correlations and

error variances/covariances set as equal between the groups will exhibit good fit to the data as

indicated by the value of 0.9 or higher of the Comparative Fit Index.

35

Hypothesis 2

Compared to English-speaking employees, Spanish-speaking employees will exhibit

statistically significant higher mean values on the latent construct of Job Content & Satisfaction,

as evidenced in an Independent Samples T-test.

The second hypothesis may be examined only if the first set of hypotheses is found to be

true, therefore confirming measurement equivalence between the Spanish and English versions of

the survey. Other forms of equivalence (i.e., sample equivalence) must be verified before

meaningful conclusions about latent construct mean differences between the samples can be

drawn.

36

METHOD

Participants

The study utilized an archival data set collected in 1999. The participants were 997

workers employed by a hospitality industry company in the South Western United States. Forty-

six percent of respondents (NEnglish = 459) completed the English version of the questionnaire and

54% (NSpanish = 538) completed the Spanish version.

Sample Equivalence

The English-speaking and Spanish-speaking samples were compared on demographic

characteristics specified in the survey, as shown in Table 8. The English-speaking and Spanish-

speaking samples were compared on six demographics using an Independent Means T-test. The

results of the demographic comparison showed that English-speaking respondents were more

likely to hold managerial, professional/technical or office/clerical jobs. Spanish-speaking

respondents were more likely to be part-time employees, to be paid hourly rather than be on salary

and to simultaneously work at another resort or have worked at another resort in the past.

Spanish-speaking respondents also tended to have longer length of service with the company.

37

Table 8.

Demographic Composition Comparison of the English- and Spanish-Speaking Samples

% o f E n g l i s h - s p e a k in g S a m p le

% o f S p a n is h - s p e a k in g S a m p le

% D if f e r e n c e

J o b F u n c t io nC o m m u n ic a t io n s / M a r k e t in g 7 . 4 0 . 6 6 . 8C o m m u n ic a t io n s / M a r k e t in g 4 . 6 0 . 7 3 . 9F a c i l i t i e s M a in t e n a n c e 2 . 8 3 0 . 2G r o u n d s M a in t e n a n c e 0 . 9 4 . 1 3 . 2G o l f C o u r s e M a in t e n a n c e 5 . 7 1 9 . 3 1 3 . 6F o o d S e r v ic e 2 5 . 3 2 3 . 2 2 . 1H u m a n R e s o u r c e s 0 . 2 0 . 2 0H o u s e k e e p in g 3 . 9 6 . 5 2 . 6G u e s t S e r v ic e s 1 1 . 5 1 1 0 . 5O p e r a t io n s 2 . 4 0 . 7 1 . 7O t h e r D e p a r t m e n t 2 5 . 3 1 1 . 9 1 3 . 4M is s in g C a s e s 1 0 1 8 . 8 8 . 8

J o b C a t e g o r yS e n io r M a n a g e m e n t 2 . 2 1 . 7 0 . 5D e p a r t m e n t M a n a g e r 1 3 . 3 1 . 3 1 2S u p e r v is o r 1 2 . 6 1 0 2 . 6P r o f e s s io n a l / T e c h n ic a l ( n o n - s u p e r v is o r y ) 1 3 . 7 3 . 2 1 0 . 5O f f ic e / C le r ic a l 1 2 . 9 0 . 7 1 2 . 2O t h e r n o n - s u p e r v is o r y e m p lo y e e 3 5 . 7 6 0 . 4 2 4 . 7M is s in g C a s e s 9 . 6 2 2 . 7 1 3 . 1

J o b S t a t u sF u l l T im e 8 5 . 4 7 1 . 6 1 3 . 8P a r t - t im e 1 0 . 9 1 3 . 6 2 . 7M is s in g C a s e s 3 . 7 1 4 . 9 1 1 . 2

E m p lo y m e n t S t a t u s ( P a y )H o u r ly 6 9 8 1 1 2S a la r ie d 2 7 4 2 3M is s in g C a s e s 4 1 5 1 1

C u r r e n t ly W o r k in g a t A n o t h e r R e s o r tY e s 6 . 1 1 7 . 1 1 1N o , b u t h a v e p r e v . w r k e d a t a n o t h e r r e s o r t 4 4 . 4 2 3 . 8 2 0 . 6N o , h a v e n e v e r w o r k e d a t a n o t h e r r e s o r t 4 5 . 1 4 1 . 6 3 . 5M is s in g C a s e s 4 . 4 1 7 . 5 1 3 . 1

L e n g th o f S e r v ic eL e s s t h a n o n e y e a r 2 5 . 7 2 0 . 1 5 . 6O n e y e a r b u t l e s s t h a n t h r e e y e a r s 2 4 1 4 . 3 9 . 7T h r e e y e a r s b u t l e s s t h a n f iv e y e a r s 1 5 . 9 1 4 . 1 1 . 8F iv e y e a r s o r m o r e 3 0 . 1 3 7 . 5 7 . 4M is s in g C a s e s 4 . 4 1 3 . 9 9 . 5

Measurement Instruments and Conceptual Equivalence

An Employee Opinion Survey developed by a private Human Resource consulting

company was used to collect the data. To measure a wide range of organizational issues, the

survey contained 87 Likert items combined into 14 scales: Work Environment, Employee

Involvement, Communications, Quality Orientation, Customer Focus, Performance Management,

Teamwork, Supervision, Leadership, Recognition & Rewards, Benefits, Career Development &

Training, Job Content & Satisfaction and Company Image & Commitment. All Likert items

utilized a five-level response scale ranging from very favorable to very unfavorable (e.g.,

�Strongly Agree�, �Agree�, �Partly Agree/Partly Disagree�, �Disagree�, �Strongly Disagree�).

Each scale also included an opt-out response option (i.e., �Don�t Know/Not Applicable�) to

38

address cases where participants could not have had an informed opinion on the matter (e.g., not

having enough length of service with the company to be eligible to receive benefits from the

employer). The opt-out option was classified as missing data.

The survey was first developed in English language by a survey consultant and signed off

by the client. The instrument was then translated into Spanish by a bi-lingual staff member of the

consulting firm. Subsequently, the translated Spanish version of the survey was scrutinized by a

bi-lingual employee in the client firm and some Spanish words were substituted by those

providing a closer meaning to the English version. That constituted the final phase of the survey

development. Copies of English and Spanish versions of the questionnaire are provided in

appendix A.

The dimensions or latent constructs of both English and Spanish version of the instruments

exhibited acceptable reliability levels (α > 0.7) as shown in Table 9.

Table 9.

Survey Dimension Reliabilities of the English and Spanish Versions of the Instrument

English Version Spanish VersionChronbach Alpha Chronbach Alpha

1 Work Environment .7669 .80482 Employee Involvement .8811 .84233 Communications .8543 .88744 Quality Orientation .7024 .79395 Customer Focus .7545 .76806 Performance Management .8468 .85347 Teamwork .7188 .79278 Supervision .9434 .94509 Leadership .9011 .9132

10 Recognition and Rewards .8913 .884311 Benefits .9285 .936312 Career Development & Training .8865 .924513 Job Content & Satisfaction .8434 .778314 Company Image & Commitment .9398 .9268

Survey Dimension

39

Data Assumptions

Missing Values

Both English and Spanish data sets had a high volume of missing values. Items from the

Benefits dimension had the highest proportion of missing data. Overall, cases without missing

values constituted 17% of the English data and 12% of the Spanish data. An exploratory data

analysis revealed that factorial structures derived with pairwise and listwise deletion were

markedly different suggesting that leaving out cases with missing data led to substantial distortion

in the results. Since pairwise deletion method is not recommended to use in SEM (Kline, 1998),

the mean imputation alternative was chosen. The factorial structure derived with the mean

substitution method closely matched that derived with pairwise deletion. The pairwise deletion,

listwise deletion and mean substitution exploratory factor analysis results for both English-

speaking and Spanish-speaking samples are displayed in Appendix B.

To estimate whether the data loss pattern was random or systematic, a Missing Data

Diagnosis feature of EQS program was used (Bentler & Wu, 1995). The program recoded raw

data file assigning all missing data a value of zero and all non-missing data a value of one. The

program then computed bivariate correlations between all combinations of variables within the

recoded data set. Positive correlation indicated that �missingness� of one variable corresponded

with the missingness of the other and zero correlation indicated that there was no joint missing

data pattern (Bentler & Wu, 1995). In addition, the Survey Language Version variable was

created with the value of one indicating the English-speaking respondents and the value of zero

indicating the Spanish-speaking respondents. The Language variable was correlated with all the

recoded variables. The low correlations between the Language variable and the survey item

40

variables indicated that language did not contribute to systematic data loss pattern: raverage=0.14,

rmax=0.27.

All demographic variables were also recoded. Each demographic variable was split into as

many variables as there were categories within that variable. For example, the variable Pay Status

had two categories: �hourly� and �salaried�. Therefore, the Pay Status variable was split into two

variables. The first Pay Status variable had values of one for all �hourly� responses and a value of

zero for all other responses. The second Pay Status variable had values of one for all �salaried�

responses and a value of zero for all other responses. To estimate whether a stance on a particular

demographic was related to data loss, all the recoded demographic variables were correlated with

the missing data pattern in each variable of the survey separately for English- and Spanish-

speaking samples. Only one demographic was found to correlate with data loss at r > 0.5. The

�hourly pay� demographic in the Spanish sample was related to the data loss in items 49, 50, 51,

53, 56. Overall, no strong evidence of the systematic data loss was found.

Multicollinearity

Examination of correlation matrices of English-speaking and Spanish speaking samples

revealed two instances of high inter-item correlations in the English-speaking sample: r12a,12b =

0.877, r12c,12b = 0.884. The highest value of squared multiple correlation coefficient with the

remainder of survey items also belonged to item 12b: R212b = 0.893 with the highest correlaries

being items 12a and 12c. These results suggest that item 12b may exhibit multicollinearity

proprieties. The tables containing correlation matrices for of English-speaking and Spanish

speaking samples can be found in Appendix D.

41

Model Specification and Identification

The original survey instrument consisted of 14 dimensions. Following the logic of

validation, all the survey dimensions were to be included in the analysis. However, sample size

put restrictions on how many parameters could be estimated in the model. The recommended ratio

of sample size to the number of model parameters is 10:1 with a minimum of 5:1 (Kline, 1998).

With the sample size NEnglish = 459 and NSpanish = 538, the maximum number of freely estimated

parameters in the model could not exceed 92. Therefore, only a subset of five survey dimensions

were included in the analysis: Communications, Leadership, Supervision, Job Content &

Satisfaction and Company Image & Commitment, resulting in a model with 85 freely estimated

parameters as discussed below.

The choice of the five dimensions was based on both empirical and theoretical rationale.

Within exploratory factor analysis, the aforementioned five dimensions exhibited strong and clean

clusters consistent across two language groups. Every survey item within the appropriate

dimension loaded onto the appropriate factor. Therefore, there was no loss of items through the

clustering procedure resulting in retention of all 35 items within those dimensions. The factor

analysis was run separately for the English- and Spanish-speaking samples. A single factor

emerged in each of Leadership, Supervision, Job Content & Satisfaction and Company Image &

Commitment dimensions. Two factors emerged in the Communications dimension. The results of

the exploratory factor analysis for the English- and Spanish-speaking groups are shown in

Appendix B.

The construct of Communications was chosen for the study because of it being a language-

related construct. The other four constructs were of interest because they received recent attention

in the cross-cultural research literature.

42

Den Hartog et al. (1999) analyzed data from the Global Leadership and Organizational

Behavior Effectiveness (GLOBE) Research Project which begun in the early 1990�s and included

60 countries. The objectives of the study were to determine which leader attributes were

universally endorsed as important to �outstanding leadership� (e.g., charismatic, inspirational,

visionary, encouraging, positive, motivational, confidence builder, team building, communicating,

coordinating) and which leader attributes were endorsed differently depending on a particular

country (e.g., risk taking, compassionate, unique, enthusiastic, sensitive) (Den Hartog et al., 1999).

Neelanavl, Mathur & Zang (2000) conducted a four-country comparison study

investigating factors affecting managerial performance (i.e., planning and decision making ability,

self-confidence and charisma, educational achievements, communication skills, past experience,

and leadership ability). The study referenced relevant research suggesting that certain underlying

task-oriented supervisory behaviors to be �genotypic� or similar in different cultures, as reflected

in replication of factorial structures across cultures, but still susceptible to some degree of cultural

influence, as reflected in difference between the latent variable means across the cultures (Misumi,

1985).

Sousa-Poza & Sousa-Poza (2001) analyzed Work Orientation data from the International

Social Survey Program (ISSP) which begun in early 1985 and included 21 countries. The study

evaluated job satisfaction as a function of interacting work inputs (e.g., education, working time,

strain, danger level) and work outputs (e.g., pay, job security, interesting work, advancement

opportunities, relationship with colleagues/managers) within the context of cross-cultural

differences (Sousa-Poza & Sousa-Poza, 2001).

Chen & Francesco (2000) studied the effects of demographic variables (i.e., organizational

position, organizational tenure, age, gender and education) on organizational commitment using

43

the methodology developed in the United States. The findings of the study in China differed from

the relevant findings in the U.S. studies which was attributed to possible China-U.S. cross-cultural

differences (Chen & Francesco, 2000). Sommer, Bae & Luthans (1996) compared antecedents of

organizational commitment (i.e., organizational position, organizational tenure and age) between

Korean and U.S. employees.

As discussed, five survey dimensions were included in the survey model:

Communications, Leadership, Supervision, Job Content & Satisfaction and Company Image &

Commitment. Based of the results of exploratory factor analysis, the Communications dimension

was split into two latent variables: [1] understanding of global organizational issues such as

company identity and its strategic orientation and [2] understanding the day-to-day internal

workings in the organization such as personnel policies and procedures, pay structures, etc.

Therefore, the study model of the was comprised of six latent variables and 35 observed variables.

The model is presented in Figure 11.

Figure 11. Six-factor model of cross-language validation of survey measurement equivalence.

Communication 2

Q14

aQ

14b

Q14

cQ

14d

Q14

e

E14a

E14b

E14c

E14d

E14e

Leadership

Q32

aQ

32b

Q32

cQ

32d

E32a

E32b

E32c

E32d

Q33

Q34

Q35

Q36

E33

E34

E35

E36

JobContent andSatisfaction

Q49

Q50

Q51

Q52

E49

E50

E51

E52

Company Image and

Commitment

Q53

Q54

Q55

Q56

Q57

Q58

Q59

E53

E54

E55

E56

E57

E58

E59

Communication 1

Q12

a

Q12

b

E12a

E12b

Supervision

Q31

aQ

31b

Q31

cQ

31d

E31a

E31b

E31c

E31d

Q31

eQ

31f

Q31

g

E31e

E31f

E31g

Q12

cE1

2c

Q13

E13

44

All latent variables in the model were set to freely covary since there was no explicit

stipulation of the relationship dynamics between the constructs. Therefore, the model contained

85 unknown parameters: 6 variances of latent variables, 15 covariances of latent variables, 35

variances of error terms and 29 regression coefficients of latent variables onto observed variables.

Following the t-rule, the models enclosed (34*35)/2=595 non-redundant observed elements.

Containing 595 known and 85 unknown elements, the model in Figure 11 was determined to be

over-identified and therefore suitable for the significance testing of the hypotheses.

Missing data analysis revealed that the reducing data sets by including variables form the

selected five dimensions only resulted in reduced rate of missing data: 51% for the English-

speaking sample and 75% for the Spanish-speaking sample (down from 83% and 88%

respectively). Reduction in relative proportion of cases with missing data was due to exclusion of

the Benefits survey dimension � the dimension containing the largest volume of missing data.

Since many employees, especially part-time employees, do not qualify for certain benefits, a high

rate of missing data within the Benefits dimension is expected. Since the rate of missing data

remained high even after the reduction of variables included in the analysis, the mean imputation

procedure was used to deal with the missing data problem.

SEM Analysis Procedure

The SEM procedures were run using the EQS 5.7b® software package (Bentler & Wu,

1995). The EQS procedure differs from the traditional LISREL procedure. The EQS approach to

multigroups analysis is known for its relative simplicity (Byrne, 1994).

It is recommended that the study model fit to the data is evaluated in each respective group

separately through establishment of a so-called single-group baseline model (Byrne, 1994). The

pattern of within-group relations between model elements may vary across the groups. Such

45

differences must be treated as a priori knowledge, rendering the varying elements non-equivalent

and therefore not specifying them as equal in subsequent multigroup model runs (Byrne, 1994).

The baseline models were established for both English- and Spanish-speaking groups.

After the establishment of the baseline models, the fit to the data was evaluated in both

groups simultaneously in the EQS multisample analysis procedure (Bentler and Wu, 1995). The

analyses estimated model fit through the successive and progressively restrictive levels of rigor by

constraining more and more model parameters as equal between the two groups. In accordance

with the multisample methodology (Byrne, 1994), at the first level of rigor, the observed

variables� loadings onto factors were set as equal across the groups and the restricted model was

evaluated for fit to the data. The Lagrange Multiplier and Wald Test procedures were used to

identify sources of invariance in the model at the first level of rigor.

At the second level of rigor, in addition to factor loadings, variances and covariances of the

seven latent variables were set as equal between the two groups and the model fit to the data was

evaluated. The Lagrange Multiplier and Wald Test procedures were used to identify sources of

invariance in the model at the second level of rigor.

Finally, on top of all previous constraints, variances of error terms were set as equal across

the two groups at the third level of rigor and the model fit to the data was again evaluated. The

Lagrange Multiplier and Wald Test procedures were used to identify sources of invariance in the

most restrictive model at the third level of rigor. The results of the analysis are discussed below.

46

RESULTS

Descriptive Statistics

Survey Items

The study utilized an archival data set with 997 cases: 459 cases from the English-speaking

group and 538 cases from the Spanish-speaking group. Since there was a large volume of missing

data in both data sets, and because SEM procedures do not allow analysis of data with missing

values, mean imputation procedure was used to fill in missing cases in the data sets. The

descriptive statistics for cases in both groups are summarized in Table 10.

Table 10.

Descriptive Statistics of the English- and Spanish-Speaking Data Sets

Surv

ey

Item

Cas

es

Cas

es

Impu

ted

Mea

n

St. D

ev.

St. D

ev.

Impu

ted

Skew

.

Skew

. Im

pute

d

Kurto

sis

Kurto

sis

Impu

ted

Cas

es

Cas

es

Impu

ted

Mea

n

St. D

ev.

St. D

ev.

Impu

ted

Skew

.

Skew

. Im

pute

d

Kurto

sis

Kurto

sis

Impu

ted

Q12A 438 459 1.96 0.76 0.73 1.05 1.08 2.28 2.54 460 538 2.12 0.90 0.83 1.27 1.37 1.92 2.76Q12B 437 459 2.01 0.80 0.77 1.13 1.16 2.25 2.51 445 538 2.16 0.89 0.80 1.19 1.30 1.75 2.73Q12C 432 459 2.05 0.84 0.81 1.25 1.29 2.46 2.80 438 538 2.15 0.94 0.84 1.28 1.42 1.84 2.94Q13 443 459 3.00 1.09 1.06 0.24 0.25 -0.62 -0.53 464 538 2.69 1.12 1.03 0.54 0.58 -0.46 -0.05

Q14A 452 459 2.73 1.10 1.09 0.69 0.70 -0.40 -0.36 501 538 2.22 0.93 0.89 1.29 1.33 1.80 2.16Q14B 443 459 2.66 1.04 1.01 0.74 0.75 -0.16 -0.06 479 538 2.56 1.10 1.03 0.80 0.85 -0.13 0.23Q14C 445 459 2.52 0.87 0.86 0.99 1.01 1.00 1.13 479 538 2.45 1.00 0.94 0.94 1.00 0.52 0.95Q14D 437 459 2.68 0.92 0.89 0.73 0.75 0.32 0.49 459 538 2.55 1.01 0.93 0.87 0.94 0.32 0.89Q14E 423 459 2.67 0.99 0.95 0.64 0.66 -0.04 0.22 444 538 2.48 1.04 0.94 0.93 1.03 0.32 1.02Q31A 449 459 2.46 1.19 1.17 0.68 0.68 -0.29 -0.23 488 538 2.79 1.20 1.13 0.44 0.46 -0.73 -0.50Q31B 449 459 2.41 1.14 1.12 0.60 0.61 -0.37 -0.31 483 538 2.84 1.14 1.08 0.34 0.36 -0.71 -0.44Q31C 443 459 2.55 1.14 1.12 0.52 0.53 -0.42 -0.32 482 538 2.78 1.14 1.07 0.43 0.45 -0.60 -0.31Q31D 447 459 2.47 1.17 1.15 0.62 0.63 -0.40 -0.33 480 538 2.59 1.11 1.04 0.64 0.67 -0.32 0.01Q31E 443 459 2.24 1.07 1.05 0.71 0.72 -0.08 0.03 478 538 2.59 1.11 1.05 0.66 0.70 -0.33 0.01Q31F 449 459 2.61 1.19 1.17 0.39 0.40 -0.67 -0.61 481 538 2.71 1.19 1.12 0.46 0.49 -0.66 -0.39Q31G 449 459 2.47 1.18 1.16 0.52 0.52 -0.56 -0.50 483 538 2.61 1.21 1.14 0.57 0.60 -0.59 -0.31Q32A 390 459 2.27 0.83 0.76 0.82 0.89 1.34 2.10 442 538 2.20 0.89 0.80 1.16 1.27 1.83 2.88Q32B 407 459 2.45 0.89 0.83 0.82 0.87 0.62 1.08 448 538 2.21 0.91 0.82 1.08 1.19 1.60 2.52Q32C 408 459 2.42 0.90 0.84 0.87 0.92 0.79 1.26 439 538 2.22 0.79 0.71 0.98 1.09 1.84 2.93Q32D 397 459 2.40 0.92 0.85 0.78 0.84 0.58 1.14 450 538 2.24 0.87 0.79 0.95 1.04 1.33 2.17Q33 425 459 2.98 1.11 1.07 0.36 0.38 -0.68 -0.50 452 538 2.50 1.09 0.99 0.86 0.94 0.15 0.75Q34 425 459 2.77 1.05 1.00 0.42 0.43 -0.37 -0.16 443 538 2.47 1.00 0.90 0.79 0.87 0.33 1.05Q35 436 459 2.96 1.17 1.13 0.29 0.30 -0.75 -0.63 454 538 2.60 1.08 0.99 0.72 0.79 -0.06 0.48Q36 431 459 2.68 1.06 1.03 0.51 0.53 -0.08 0.11 452 538 2.37 1.00 0.91 0.86 0.94 0.59 1.27Q49 451 459 1.81 0.77 0.76 1.23 1.24 2.76 2.86 482 538 1.89 0.74 0.70 1.24 1.31 3.17 3.88Q50 448 459 2.05 0.95 0.94 1.01 1.02 1.01 1.11 475 538 2.37 0.95 0.89 0.91 0.97 0.62 1.10Q51 449 459 2.13 1.06 1.05 1.08 1.09 0.82 0.91 478 538 2.31 0.97 0.91 0.91 0.97 0.56 1.01Q52 427 459 2.35 0.99 0.95 0.73 0.75 0.55 0.82 387 538 2.32 0.91 0.77 0.89 1.04 1.16 2.77Q53 445 459 2.44 0.99 0.97 0.58 0.59 0.06 0.16 476 538 2.29 0.94 0.88 0.99 1.06 1.06 1.59Q54 430 459 2.57 1.03 0.99 0.69 0.72 0.05 0.25 473 538 2.33 0.91 0.85 0.84 0.89 0.61 1.11Q55 433 459 2.58 1.13 1.09 0.57 0.58 -0.27 -0.11 457 538 2.39 0.99 0.90 0.82 0.88 0.50 1.12Q56 442 459 2.29 0.98 0.96 0.75 0.76 0.48 0.62 465 538 2.27 0.98 0.90 0.97 1.04 0.96 1.58Q57 445 459 2.40 0.98 0.96 0.60 0.61 0.23 0.33 473 538 2.29 0.96 0.89 0.94 1.00 0.91 1.45Q58 444 459 2.66 1.15 1.13 0.49 0.50 -0.43 -0.34 462 538 2.59 1.08 0.99 0.57 0.61 -0.29 0.16Q59 409 459 2.60 0.94 0.88 0.31 0.33 0.01 0.38 421 538 2.47 1.02 0.89 0.61 0.69 0.29 1.20

English-Speaking Sample Spanish-Speaking Sample

47

The results on mean imputation with decrease in variables� standard deviations and

increase in skewness and positive kurtosis values did not appear to have led to large distortions in

the data. The distortions were larger in the data of the Spanish-speaking sample due to the higher

volume of missing data in that group. Overall, five variables in the English-speaking sample (i.e.,

12a, 12b, 12c, 32a and 49) and ten variables in the Spanish-speaking sample (12a, 12b, 12c, 14a,

32a, 32b, 32c, 32d, 49 and 52) had excessive positive kurtosis suggesting non-normal distribution

of these variables.

Survey Dimensions

To evaluate the properties of the survey dimensions, a Principal Components Factor

Analysis procedure with Varimax Rotation was utilized separately for the English- and Spanish-

speaking data sets. The detailed results of the factor analyses are presented in Appendix B and

summary of the results is presented in Table 11. Analyses in both data sets yielded 17 and 16

components with eigenvalues of more than one in the English-speaking and Spanish-speaking data

sets respectively. The extracted components accounted for 70.9% and 70.5% of total variance in

the English-speaking and Spanish-speaking data sets respectively.

Table 11.

Exploratory Factor Analysis Results for the English- and Spanish-speaking Data Sets

Benefits 1 3 3.5 4.023 44.921 5.435 6.247 22.663 Company Image & Commitment 3 3.405 3.914 45.425 6.186 7.111 22.896Customer Focus + Teamwork 4 2.894 3.326 48.247 4.336 4.984 27.647 Career Development & Training 4 3.096 3.559 48.984 5.281 6.07 28.966Career Development & Training 5 2.308 2.653 50.9 4.035 4.638 32.285 Leadership 5 2.457 2.824 51.808 5.201 5.978 34.944Rewards & Recognition 6 2.156 2.478 53.379 3.891 4.473 36.758 Rewards & Recognition 6 2.121 2.438 54.246 4.09 4.702 39.646Leadership 7 1.882 2.164 55.542 3.817 4.388 41.146 Communications 2 7 2.019 2.321 56.567 3.89 4.471 44.117Employee Involvement 1 8 1.841 2.116 57.658 3.712 4.266 45.412 Performance Management 8 1.751 2.012 58.579 3.522 4.048 48.165Benefits 2 9 1.675 1.926 59.584 3.118 3.584 48.996 Communications 1 9 1.647 1.893 60.472 3.286 3.777 51.942Communications 1 10 1.539 1.769 61.353 2.812 3.232 52.228 Work Environment 10 1.492 1.715 62.187 3.128 3.595 55.537"Various" 11 1.39 1.597 62.95 2.799 3.217 55.445 Employee Involvement 11 1.443 1.658 63.846 2.969 3.413 58.95Job Content & Satisfaction 12 1.278 1.468 64.419 2.718 3.124 58.569 Quality Focus + Customer Focus 12 1.253 1.44 65.286 2.856 3.283 62.232Communications 2 13 1.249 1.436 65.854 2.521 2.898 61.467 Teamwork 13 1.204 1.383 66.669 2.193 2.521 64.753Performance Management 14 1.149 1.321 67.175 2.501 2.875 64.342 Job Content & Satisfaction 14 1.132 1.301 67.97 1.955 2.247 67.001Work Environment 15 1.107 1.272 68.447 2.476 2.846 67.188 Benefits 2 15 1.114 1.281 69.251 1.862 2.141 69.141Employee Involvement 2 16 1.05 1.207 69.654 2.012 2.313 69.501 "Various" 16 1.023 1.176 70.427 1.119 1.286 70.427"Various" 17 1.039 1.195 70.848 1.173 1.348 70.848

Compo- nent Total

% of Variance Cumul. % Total

% of Variance Cumul. % Compo-nent Total

% of Variance Cumul. %

pany Image & Commitment 1 30.498 35.055 35.055 7.489 8.608 8.608 Supervision 1 31.809 36.562 36.562

Survey Dimensions Survey DimensionsTotal

% of Variance Cumul. %

Com 7.013 8.061 8.061Supervision 2 5.083 5.842 40.897 6.794 7.809 16.416 Benefits 1 2 4.306 4.95 41.511 6.72 7.724 15.785

English-Speaking Sample Spanish-Speaking SampleInitial Eigenvalues Rotation Sums of Squared Loadings Initial Eigenvalues Rotation Sums of Squared Loadings

48

As described in the methods section, five survey dimensions were chosen for the study:

Leadership, Supervision, Communications, Job Content & Satisfaction and Company Image &

Commitment. The results of factor analysis indicated that the Communications dimension was

comprised of two factors in both language groups. Therefore, two communication latent variables

were specified in the study model. With this adjustment, each of the six chosen factors exhibited

strong and clean clusters consistent across two language groups. Every survey item within the

appropriate dimension loaded onto the appropriate factor. Therefore, there was no loss of items

through the clustering procedure resulting in retention of 35 items within six latent

variables/dimensions (i.e., q12a, q12b, q12c, q13, q14a, q14b, q14c, q14d, q14e, q31a, q31b,

q31c, q31d, q31e, q31f, q31g, q32a, q32b, q32c, q32d, q33, q34, q35, q36, q49, q50, q51, q52,

q53, q54, q55, q56, q57, q58, q59). The six chosen latent variables exhibited acceptable reliability

coefficients as shown in Table 12.

Table 12.

Reliabilities of the Chosen Survey Dimensions of the English and Spanish Data Sets

Survey Dimension English Version Spanish VersionChronbach Alpha Chronbach Alpha

1 Communications 1 0.7908 0.83542 Communications 2 0.8212 0.87113 Supervision .9434 .94504 Leadership .9011 .91325 Job Content & Satisfaction .8434 .77836 Compant Image & Commitment .9398 .9268

Baseline Model � Single-Group Analysis

It is recommended that the study model fit to the data is evaluated in each comparison

group separately through so-called establishment of a single-group baseline model (Byrne, 1994).

To establish the baseline model for the English-speaking sample, the six-factor model was

imposed onto the data of the English-speaking respondents. The partial EQS run output for the

49

model of the English-speaking group can be found in Appendix E. The single-group analysis

revealed the model covariance matrix to be nonpositive definite, suggesting problems in the data.

The obvious choice in addressing the problem in the data was to eliminate the

multicollinearity case combining items 12a and 12b. As mentioned in the Method section,

examination of correlation matrices of the English-speaking sample revealed two instances of high

inter-item correlations: r12a,12b = 0.877, r12c,12b = 0.884 (see Appendix D). The highest value of

squared multiple correlation coefficient with the remainder of survey items also belonged to item

12b: R212b = 0.893 with the highest correlaries being items 12a and 12c, suggesting that item 12b

may exhibit multicollinearity proprieties. Also, items 12a (�I understand company�s vision�) and

12b (�I understand company�s mission�) are close in content. Therefore, there were empirical and

theoretical reasons to combine items 12a and 12b into a single item 12ab.

The same six-factor model was then imposed onto the adjusted data set of English-

speaking respondents. In that instance, the model parameter covariance matrix was positive

definite suggesting no remaining problems in the data. The overall model fit to the data was

adequate (CFIEnglish-Sample-Initial-Baseline-Model = .911). The partial EQS output for the model run with

the adjusted data set can be found in Appendix F.

There were two caveats to the model. First, as mentioned in the univariate Descriptive

Statistics segment of the Results section, four items exhibited excessive positive kurtosis: 12c,

32a, 49 as well as the combined item 12ab, suggesting non-normality of these items. However,

SEM procedures are not easily affected by the violation of the non-normality assumption (Bollen,

1989). Therefore, the non-normal items were retained in the model. Also, multivariate kurtosis

analysis did not reveal any case-outliers in the data. Second, the Lagrange Multiplier test

indicated that the model fit could be substantially improved by allowing item 52 �considering

50

everything I am satisfied with my job� from the Job Content & Satisfaction dimension to cross-

load onto the Company Image & Commitment latent variable. Such cross-loading was also

supported empirically by the exploratory factor analysis (see Appendix B).

The English-speaking sample six-factor model was respecified allowing item 52 to cross-

load onto the Company Image & Commitment latent variable. The resulting model fit to the data

showed a significant improvement (CFIEnglish-Sample-Final-Baseline-Model = .923). The baseline model for

the English-speaking sample was therefore established. The complete EQS output for the final

base model run for the English sample with the adjusted data set can be found in Appendix G.

To establish the baseline model for the Spanish sample, the initial six-factor model was

imposed of the data on the Spanish-speaking respondents. The single-group analysis revealed the

model covariance matrix to be positive definite, suggesting no serious problems in the data.

As in the English-speaking sample, there were caveats to the model-data fit of the Spanish-

speaking respondents. The univariate descriptive statistics analysis showed excessive positive

kurtosis for nine items: 12a, 12b, 12c, 14a, 32a, 32b, 32c, 32d, 49 and 52, suggesting non-

normality of these items. However, SEM procedures are not easily affected by the violation of the

non-normality assumption (Bollen, 1989). Therefore, the non-normal items were retained in the

model. Also, multivariate kurtosis analysis of the Spanish-speaking respondents� data revealed an

outlier - case # 5 - which had to be removed from the data set. Finally, the cross-loading of item

52 onto the Company Image & Commitment latent variable showed to significantly improve the

model fit. The partial EQS run output for the model can be found in Appendix H.

Using the same rationale as for the English-speaking sample, the model for the Spanish-

speaking sample was respecified allowing item 52 to cross-load onto the Company Image &

Commitment latent variable. The highly-correlated items 12c and 12b were combined into a

51

single variable 12ab. In addition, case # 5 was removed as a multivariate outlier. The respecified

six-factor model was imposed onto the adjusted data set from the Spanish-speaking sample. The

overall model fit to the data was adequate (CFISpanish-Sample-Final-Baseline-Model = .916). The baseline

model for the Spanish-speaking sample was therefore established. The complete EQS output for

the final base model run for the Spanish-speaking sample with the adjusted data set can be found

in Appendix I.

Multigroup Analysis � Phase 1

The base models from the English- and Spanish-speaking samples were combined in a

single multigroup measurement and structural model. The partial EQS run output of the initial

multigroup model can be found in Appendix J. The analysis revealed the initial multisample

model covariance matrix to be nonpositive definite, suggesting that there were remaining

problems in the data perhaps at a deeper level of the parameter relations� system. In order to

locate the misfit in the larger model, it was broken into submodels each constituting a single latent

variable with the corresponding observed variables (except for Communications dimension model

which consisted of two latent variables and corresponding observed variables). Each submodel

was fit to the data as a single-group model within the respective language data set. Ten submodels

were considered overall. The graphic representation of the submodels is shown in Figure 12

through Figure 16.

Figure 12. Single survey dimension submodel: Communications.

Communication 2

Q14aQ14bQ14cQ14dQ14e

E14aE14bE14cE14dE14e

Communication 1 Q12abQ12c

E12abE12c

52

Figure 13. Single survey dimension submodel: Supervision.

Supervision

Q31aQ31bQ31cQ31d

E31aE31bE31cE31d

Q31eQ31fQ31g

E31eE31fE31g

Figure 14. Single survey dimension submodel: Leadership.

Leadership

Q32aQ32bQ32cQ32d

E32aE32bE32cE32d

Q33Q34Q35

E33E34E35

Q35 E35

Figure 15. Single survey dimension submodels: Job content & satisfaction.


Q49Q50Q51Q52

E49E50E51E52

Figure 16. Single survey dimension submodels: Company image & commitment.

Company Image and

Commitment

Q53Q54Q55Q56

E53E54E55E56

Q57Q58Q59

E57E58E59

The fit to the data of each submodel was estimated through the value of the Comparative

Fit Index with 0.9 < CFI < 1.0 indicating a good model fit to the data (Bentler, 1995). The results

for CFI values for the ten submodels are summarized in Table 13.

53

Table 13.

Single-Survey-Dimension Models� Fit for English and Spanish Samples

# Single Survey Dimension Submodel Model CFI (English)

Model CFI (Spanish)

1 Communications 0.954 0.9732 Supervision 0.963 0.9783 Leadership 0.883 0.8884 Job Content & Satisfaction 1.000 0.9785 Company Image & Commitment 0.988 0.977

Out of all submodels, the Leadership model had less than adequate fit to the data in both

English and Spanish samples. There was no clear indication on the sources of misfit in the

Leadership dimension. Since one of the main criticisms of the SEM approach is its explanation of

only one of the many possible alternatives, it is important that model specification and

respecification is based on a theoretical rationale to a maximum extent possible and not just being

guided by abstract statistical output (Bollen, 1989).

Conceptually, the eight items comprising the Leadership dimension could be looked at as

two clusters: [1] items 32a, 32b, 32c and 32d representing perceptions of how management

handles specific aspects of the business or tactical aspects of leadership and [2] items 33, 34, 35,

36 representing perception of broad, strategic aspects of leadership such as providing strategic

direction and overall global management of the company and its people. Driven by the item

content analysis, the single-latent-variable model of Leadership was respecified as a two-latent-

variable model shown in Figure 13. The respesified model showed a dramatic improvement in fit

to the data: CFI2-Factor-Leadership-English-Model= 0.975 and CFI2-Factor-Leadership-Spanish-Model= 0.949.

54

Figure 17. Two-factor model for the Leadership dimension.

Leadership 2

Leadership 1

Q32aQ32bQ32cQ32d

E32aE32bE32cE32d

Q33Q34Q35

E33E34E35

Q35 E35

Multigroup Analysis � Phase 2

Based on the results of the respecification of the single-latent variable Leadership model

and incorporating the requirement of cross-loading of item 52 onto the Company Image &

Commitment construct, the multigroup model was respecified as shown in Figure 14. The seven-

factor model contained 89 unknown parameters: 7 variances of latent variables, 21 covariances of

latent variables, 34 variances of error terms and 27 regression coefficients of latent variables onto

observed variables. Containing 595 known and 89 unknown elements, the model in was over-

identified.

Figure 18. Seven-factor multisample study model.

Communication 2

Q14

aQ

14b

Q14

cQ

14d

Q14

e

E14a

E14b

E14c

E14d

E14e

Leadership 1

Q32

aQ

32b

Q32

cQ

32d

E32a

E32b

E32c

E32d

Leadership 2

Q33

Q34

Q35

Q36

E33

E34

E35

E36


Q49

Q50

Q51

Q52

E49

E50

E51

E52

Company Image and

Commitment

Q53

Q54

Q55

Q56

Q57

Q58

Q59

E53

E54

E55

E56

E57

E58

E59

Communication 1

Q12

ab

Q12

c

E12a

b

E12c

Supervision

Q31

aQ

31b

Q31

cQ

31d

E31a

E31b

E31c

E31d

Q31

eQ

31f

Q31

g

E31e

E31f

E31g

Q13

E13

55

The seven-factor model was imposed onto the data of the English- and Spanish-speaking

samples. The fit to the data was evaluated in both groups simultaneously in the EQS multisample

analysis procedure (Bentler and Wu, 1995). Following the logic of Multisample Analysis, the

model fit was estimated through three successive and progressively restrictive levels of rigor by

gradually constraining the following model parameters as equal between the two groups: observed

variables loadings onto respective factors, factor variances/covariances and variances of error

terms. The Lagrange Multiplier and Wald Test procedures were used to identify sources of

invariance in the model at each of the three levels of rigor suggesting adjustment in parameters to

improve model fit to the data. The detailed description of the multisample analysis procedure is

presented below.

At the first level or rigor, the constraint of equal item loadings onto respective factors was

imposed onto the seven-factor multigroup model. The model yielded a positive definite parameter

covariance matrix, suggesting no serious problems in the data as related to the model specification.

The model fit to the data was adequate (GFIMultisample-Model-1 = .930). The convergence onto the

solution was quick requiring only four iterations. The maximum number of iterations in

calculating the fitting function in EQS is 30 (Byrne, 1994). After 30 unsuccessful iterations, the

program displays a message that a solution was not attained due to a range of potential problems

in the model. However, a convergence onto a solution within seven iterations is considered to be

quick and good (Byrne, 1994).

The Lagrange Multiplier test within the model at the first level of rigor indicated that the

model fit could be improved at a statistically significant level by removing equality constraints of

the seven following items: 14c, 32b, 32c, 32d, 52, 55, 58. However, upon the removal of the

56

identified constraints, the resulting model fit did not show a meaningful improvement (∆CFI =

.003). Therefore the constraints identified by the Lagrange Multiplier test were not dropped.

At the second level of rigor, moving to a more restrictive model while incorporating the

constraints of the first level of rigor, the equality of item loadings onto respective factors and

equality of factor variances and covariances were imposed onto the multigroup model. The model

parameters� covariance matrix was positive definite. The model fit to the data was adequate

(GFIMultisample-Model-2 = .926). The convergence onto the solution was quick requiring only five

iterations. The Lagrange Multiplier output indicated that the model fit could be improved at a

statistically significant level by removing 20 out of 56 the equality constraints mostly related to

relationships between latent variables. However, removing the 20 identified parameters from the

model resulted in a negligible improvement in model fit (∆CFI = .007). Therefore the constraints

identified by the Lagrange Multiplier were not dropped. Hence, at the second level of rigor,

Hypothesis 1a was confirmed. The two language versions of the survey exhibited invariance of

factor structures of the selected survey dimensions. Patterns of item loading onto factors and

patterns of factor cross-correlations were found similar across the language groups.

At the third level of rigor, moving to the most restrictive model incorporating the

constraints of the first and second levels of rigor, the equality of item loadings onto respective

factors, equality of factor variances and covariances and equality of error variances were imposed

onto the multigroup model. The model parameters� covariance matrix was positive definite. The

model fit to the data was adequate (GFIMultisample-Model-3 = .920). The convergence onto the solution

was quick requiring only four iterations. The program output indicated that all equality constraints

were correctly imposed. All the estimated parameter values were identical across both groups.

All parameters were found statistically significant. The matrices of residuals (S - Σ(θ)) did not

57

exhibit overly large values. The rule of thumb in SEM modeling is a residual of more than 0.1

indicating that a particular variable in the model does not depict the corresponding relationship in

the data well (Byrne, 1994). Although there is not clear indication in the literature how big a

proportion of large residuals is acceptable in the model, it should be ass small as possible (Byrne,

1994). In the English sample, residuals with values more than 0.1 made up 4% of the residuals�

total; in the Spanish sample, residuals of more than 0.1 constituted 13% of the total. The final

version of the multigroup model yielded average standardized residual values of 0.06 for the

English-speaking sample and 0.05 for the Spanish-speaking sample which indicated a relatively

good fit to the data. The distributions of standardized residuals were symmetric and centered

around zero for both groups which is an indicator of absence of model specification problems.

The Lagrange Multiplier test within the most restrictive model indicated that the model fit

could be improved at a statistically significant level by removing 26 out of 90 equality constraints

mostly related to relationships between latent variables. However, removing all the 26 identified

parameters from the model resulted in a negligible improvement in model fit (∆CFI = .012).

Therefore the constraints identified by the Lagrange Multiplier were not dropped. The complete

EQS run output of the final most restrictive multigroup model can be found in Appendix K.

Hence, at the third level of rigor, Hypothesis 1b was confirmed. A model incorporating both

English and Spanish groups with [1] item loadings, [2] factor cross-correlations and [3] error

variances/ covariances set as equal between the groups exhibited a good fit to the data as indicated

by the value of 0.92 of the Comparative Fit Index.

The seven-latent-variable multigroup model passed the three levels of rigor necessary to

confirm measurement equivalence of the English and Spanish versions of the survey instrument.

The model fit to the data was found adequate (GFIFinial-Multisample-Model = .92) falling within the

58

acceptable range of �good� model fit to the data: 0.90 < CFI < 1.00 (Bentler, 1995). Data

collected by the two language versions of the survey did not exhibit systematic data loss patterns.

Therefore, the five following dimensions in the Spanish and English versions of the employee

opinion survey were found equivalent: Communications, Leadership, Supervision, Job Content &

Satisfaction and Organization Image & Commitment.

Multisample Model Parameters Contributing to Reduction in Model Fit

Although the final model fit to the data was found adequate, the assessment of parameter

misspecification via the Lagrange Multiplier test revealed that removing equality constraints from

almost one-third of model parameters could have significantly improved model-to-data fit.

Overall, 26 model parameters were found to contribute to model misfit at a statistically significant

level. However, the suggested non-equivalence of the aforementioned observed and latent

variables needed to be considered in terms of practicality.

Byrne (1994) states that in deciding which parameters to free, a researcher �walks a thin

line between adequately fitting the model and overfitting the model� (p. 85). According to Byrne

(1994), such decisions should rely on theory or past empirical findings. Therefore, relying on a

combination of factors, a researcher makes a judgment call whether to include or exclude certain

parameters from the model. One of the guiding principles of whether to select certain parameters

out of the pool identified by the Lagrange Multiplier test is their �standing out� by yielding

exceptionally large contribution to model misfit compared to all other identified misfit parameters.

Since the EQS program output sorts all the misfit parameters in order of the size of their

contributed misfit, like a layer of crème on top of milk, the largest contributors to misfit rise to the

top of the list. There is usually a natural cutoff in the sequence of parameters where the

59

contribution model to misfit tapers off sharply. Such break could serve as a cutoff for selecting

misfit parameters for removal from the model (Byrne, 1994).

Despite the large number the misfit parameters identified in the study model by the

Lagrange Multiplier multivariate test, the cumulative improvement in fit when removing these 26

parameters from the model would be relatively small (∆CFI = .012). Except for the cross-loading

of item 52 onto the Company Image & Commitment dimension � the parameter that contributed

exceptionally large value to model misfit � none of the model individual parameters were found to

substantially affect the model fit (see Appendix K). Therefore, there was no empirical reason for

removing the discovered misfit parameters from the model.

Five out of the eight items that contributed to model misfit at a statistically significant level

(i.e., 12c, 32b, 32c, 32d, 52) exhibited non-normal univariate response distributions suggesting a

possible relationship between violation of the normality assumption and model misfit. Estimates

of multivariate kurtosis via z-statistics also pointed to non-normality in the data of both samples:

ZMultivariate-Kurtosis-Normalised-Estimete-English = 76.55 and ZMultivariate-Kurtosis-Normalised-Estimete-Spanish = 127.61.

However, SEM procedures are known to be robust against violation of normality (Bollen, 1989).

Comparison of Job Satisfaction Means Between the Language Groups

Since the two language versions of the survey were found to be equivalent, the survey

dimension/scale means of the English- and Spanish-speaking groups could be compared. The

scale scores of Job Content & Satisfaction were compared using the Independents Means T-test.

There was a significant difference between the English- and Spanish-speaking respondents

t(936) = 3.8, p < .001. Since the raw data values are coded in the following way: 1= �Strongly

Agree�, 2 = �Agree�, 3 = �Partly Agree/Disagree�, 4 = �Disagree� and 5 = �Strongly Disagree�,

the smaller mean values indicate higher agreement and satisfaction. Hence, the Hypothesis 2 was

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not confirmed. Compared to English-speaking employees, Spanish-speaking employees exhibited

statistically significant higher (or less agreeable) mean values on the latent construct of Job

Content & Satisfaction: MeanEnglish=1.998, SDEnglish=0.791; and MeanSpanish=2.187,

SDSpanish=0.732. However, in practical terms, there was no meaningful difference between the

English- and Spanish-speaking respondents.

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DISCUSSION

The current study evaluated measurement equivalence of five dimensions of the English

and Spanish versions of employee opinion survey administered in a hospitality industry company

in the South Western United States. Based on prior research (Jöreskog & Sörbom, 1989; Lytle et

al., 1995), the concept of measurement equivalence was defined consisting of four components:

[1] sample equivalence (comparability of samples in the study on key demographic

characteristics), [2] semantic equivalence (rigorous translation methods of the survey instrument

from one language to another), [3] conceptual equivalence (similar interpretation of the respective

translated survey items by different cultural groups) and [4] scalar equivalence (similar

interpretation of the respective translated response scales by different cultural groups).

Sample equivalence may or may not affect the results of other measurement equivalence

tests (i.e., conceptual and scalar equivalence). Therefore, sample equivalence serves as a control

factor and can be assessed before or after other tests of measurement equivalence, providing an

explanation of why a particular result with respect to measurement equivalence was achieved.

Cultural groups� differing on key demographic factors (e.g., education level) may affect

individuals� ability to interpret survey items therefore confounding analyses of measurement

equivalence. The results of the current study showed that difference in demographic composition

of the English-speaking and Spanish-speaking samples did not affect the conceptual and scalar

equivalence of two versions of the survey instrument. English-speaking respondents were more

likely to hold managerial, professional/technical or office/clerical jobs. Spanish-speaking

respondents were more likely to be part-time employees, to be paid hourly rather than be on salary

and to simultaneously work at another resort or have worked at another resort in the past.

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Unlike sample equivalence, semantic equivalence is a prerequisite to testing conceptual

and scalar equivalence. Rigorous translation methodology is a fundamental starting point in

ensuring equivalence of measures. Semantic equivalence is, therefore, a necessary but not

sufficient condition to establishing measurement equivalence. The survey instrument used in the

current study undergone two developmental stages. First, the original survey instrument

developed in English language was translated into Spanish. Then, the translation was verified by

the second independent translator. Semantic equivalence of the English and Spanish versions of

the survey instrument was assumed to have been achieved.

To assess conceptual and scalar equivalence of the two version of the survey, the study

utilized Multigroup Analysis in Structural Equation Modeling (SEM). EQS 5.7b® software

package was used to run the analyses (Bentler, 1995). With respect to conceptual equivalence, it

was hypothesized that the English and Spanish versions of the survey were equivalent implying

similarity of factorial structures in both English- and Spanish-speaking data sets. With respect to

scalar equivalence, it was hypothesized that the English and Spanish versions of the survey were

equivalent implying similarity of error terms� variances of corresponding survey items across the

two language groups.

Before running SEM analyses, certain data proprieties had to be verified to quality for use

in SEM process. Sample size, the overall proportion of missing data, degree of intercorrelation

between variables and normality of variables� distributions were evaluated.

Sample size put a limitation to the number of variables that could be estimated in the SEM

model. Out of 14 original survey dimensions containing 87 items, five dimensions

(Communications, Leadership, Supervision, Job Content & Satisfaction and Company Image &

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Commitment) containing 35 items were selected. Selection of the subset of dimensions relied on

both empirical and theoretical rationale.

The study found that a high rate of missing data affected the factorial structures of both

English- and Spanish-speaking data sets. There was no evidence found that the data loss pattern

was systematic. A mean imputation procedure was used to correct for missing data: each missing

data point was substituted by the variable average.

The study also found a very high level of correlation among two items: �I understand [my

company�s] mission� and �I understand [my company�s] vision�, which confounded SEM�s

algorithms. The two items were combined into one variable, which made sense since the items

were very close in content. The adjustment allowed SEM to run smoothly.

Nine survey items exhibited excessive positive kurtosis and five out of those nine items

contributed to the reduction in equivalence between the two language versions of the survey,

suggesting that violation of the assumption of normal distribution of the observed variables could

have affected the results. Although, SEM procedures are known to be robust against violation of

normality (Bollen, 1989).

Overall, SEM analyses revealed that the factorial structure of the survey data did not

replicate the survey dimension structure exactly in that there were two bimodal dimensions found:

Communications and Leadership. The Communications dimension consisted of two factors: [1]

understanding of company�s identity (i.e., mission, vision, culture) and [2] perception of company-

wide communication processes� effectiveness in disseminating essential organizational

information (e.g., company policies, financial performance, etc.). The Leadership dimension also

consisted of two factors: [1] strategic leadership - perception of senior management providing

strategic direction and overall global management of the company and its people and [2] tactical

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leadership - perceptions of how senior management handles specific aspects of the business (e.g.,

controlling costs, implementing changes to compete effectively). Despite the fact that factorial

structure of the survey data in both language groups did not replicate the original survey

dimension structure, both groups� response patterns deviated from the survey structure in similar

ways: both data sets yielded seven-cluster structure.

Overall, the Multigroup Analysis revealed that the English and the Spanish versions of the

survey exhibited acceptable measurement equivalence across the five dimensions. There were no

items found indicating clear non-equivalence between the two language groups.

The results of the study validated the quality of the survey instrument translation. Most of

the Spanish-speaking workers were from Mexico. Since the United States and Mexico are

neighboring countries, it could be argued that there is sufficient information flow between these

countries allowing for similarity in their members� conceptualization of various phenomena in the

workplace in the hospitality industry.

Based on prior research (Reinemer, 1995; Lankau & Scandura, 1996), it was hypothesized

that Spanish-speaking respondents would have higher satisfaction scores on the Job Content &

Satisfaction survey dimension. However, that hypothesis was not confirmed. The Independent

Samples T-test showed that the Job Satisfaction mean score of Spanish-speaking respondents was

higher (less agreeable) than that of the English-speaking respondents at a statistically significant

level. However, the statistical significance of the results is explained by the large sample size. In

practical terms, there was no meaningful difference found between the English- and Spanish-

speaking respondents.

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Limitations of the Study

As mentioned previously, there was a relatively high volume of missing values in the data:

51% of the cases in the English-speaking sample and 75% of cases in the Spanish-speaking

sample had missing values. Since SEM procedures cannot accommodate missing values in

analyses, cases with missing data had to be either discarded or substituted by an imputation

procedure. Discarding cases was not an option because they were so numerous that their deletion

distorted the factorial structure of the data. Therefore, the mean imputation procedure was utilized

where missing values in a case were substituted by the average of the corresponding variable.

However, this procedure is not the best substitution method in that it is considered to be lacking

precision. Mean imputation results in reduction of variables� variance and, therefore, could impact

coefficients in the correlation and covariance matrix. Comparatively, regression imputation

procedure could offer more refined modification of the data. However, under the circumstances,

mean imputation was a procedure of choice for the current study.

The original survey instrument used in the study consisted of 14 dimensions or scales.

Applying the logic of the study, the entire instrument needed to be evaluated for measurement

equivalence. However, an SEM model incorporating all 14 scales would contain an excess of 300

parameters to be evaluated. For results of the SEM analyses to be stable, a minimum required

case-to-parameter ratio is 5 to 1 with a recommended ratio of 10 to 1 and an ideal ratio of 20 to 1.

Therefore, a single-group model including the entire survey instrument in the study would require

a minimum of 1500 valid cases, and a multigroup model would need a data set with 3000 cases.

Since there were only 997 cases available for this study (NEnglish=459, NSpanish=538), a

subset of five dimensions was chosen for analysis resulting in a model with 89 parameters to be

evaluated. Selection of the dimensions for the study was based partly on empirical rationale.

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These dimensions had the factorial clusters that appeared most consistent across the language

groups (see Appendix B). It very well may be that other less consistent dimensions (e.g.,

Performance Management, Guest Focus) would not show equivalence between the groups.

During SEM analyses, it was determined that the survey dimension of Leadership was

bimodal consisting of two latent variables: strategic leadership and tactical leadership. Splitting

the Leadership dimension into two factors was driven primarily by empirical and not theoretical

rationale. Relying on a priori theoretical assumption when specifying relationships between

variables is an axiom of research methodology. Post hoc adjustments cold be criticized as a

�fishing expedition�. Nevertheless, although derived empirically, the two new Leadership factors

yielded a common-sense interpretation of strategic and tactical leadership orientations.

Finally, to increase certainty in obtained results, a range of fit indices (e.g., SRMR, GFI,

CFI) could be utilized. The current study used a single model fit index CFI available in the EQS

5.7b® software package.

Implications for Research and Practice

Evaluating measurement equivalence through Multisample Analysis in Structural Equation

Modeling is a demanding procedure, both conceptually and technically. The good news is,

however, that it is becoming easier and easier to use the methodology of SEM. The user-friendly

introductory materials about SEM have been written (e.g., Kline 1998). That does not negate the

complexity of the method, but it makes the study of it a less daunting task. There is also an active

community of SEM users that communicates over the Internet. The SEM user can get advice and

get answers to his/her questions there. SEM software is also becoming more diversified, user-

friendly and sensitive to consumer needs. In fact, the Multisample Analysis procedure in EQS is

pretty straight-forward (Byrne, 1994).

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Besides the method know-how, a researcher, and especially a practitioner, faces multiple

data-related hurdles when using SEM. The procedures of Structural Equation Modeling require

large sample sizes. Working with a survey instrument of 50 to 60 items, which is a reasonable

length in practice, would require 2000-4000 valid cases to be able to run SEM algorithms with a

model large enough to incorporate the entire 60-item survey. Since some practitioners structure

consulting fees based on number of survey respondents in the project, requirements for large

sample sizes could directly affect project budgets driving up the costs of survey printing,

distribution and data collection. Alternatively, faced with a practical restriction of insufficient

sample size, a researcher may break up the model and apply its smaller components to the

available data set, as was done in the current study. Fitting a partial model to the data is a less

stringent test compared to evaluating a full model simultaneously considering all the variables

specified in the survey. However, a less stringent test is better than no test at all.

The reality of collecting and analyzing data via employee surveys involves large volumes

of missing data. Invalidating cases with missing data could dramatically affect sample size, which

is a sensitive issue with SEM, and may not be practical since it would eliminate valid information

crucial to a practitioner. The alternative is to use missing data substitution methods and those may

vary in the degree of distortion they impose on the data.

Finally, SEM appears sensitive to certain data proprieties, such as multicollinearity. In the

current study, a single case of multicollinearity between two variables caused the SEM procedures

to fail. A researcher must screen the data carefully before running SEM algorithms.

Overall, the process of specifying a model which is to be imposed onto the data and

choosing which model parameters to retain and which to discard when striving to improve model-

data fit has a strong subjective component to it. A researcher must rely on his/her knowledge of

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the theory involving the subject matter. All in all, an SEM model is only a single version of

�reality� as chosen by the researcher. The fact that a model yields a good or excellent fit to the

data does not constitute proof that the model is an accurate depiction of reality, but rather gives a

reason for cautious optimism that it might be the case. Replication of study results and accumula-

tion of knowledge over time is the strongest guarantee that the model represents reality well.

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CONCLUSIONS

The assumption that fundamental mental processes such as perception, memory, causal

analyses, inference, etc. are a part of a core human characteristic independent of cultural

background and expression in the language has been found incorrect (Nisbett, 2003). Despite that

patterns of mental work are relatively stable phenomena because cultural factors and thinking

habits form mutually reinforcing systems, it has been proven that fundamental mental processes

are not rigid and can be influenced by socio-cultural factors in a relatively short time (Nisbett,

2003). More so, individuals who were transplanted from one culture to another were shown to

shift between culturally-bound mental models depending on the environment in which they found

themselves (Nisbett, 2003). This evidence suggests a complex picture of cross-cultural mental

models� divergence that becomes more relevant and consequential as the global community

continues to integrate at an accelerated pace through advances in communication technology,

proliferation of market economy, prevalence of economic priorities over the political ones, and

burgeoning of transnational corporations which create a demand for highly interactive and well-

integrated multicultural workforce.

As multinational companies are trying to address the issues of organizational effectiveness

and alignment of organizational functions across borders, the subject of robust cross-national

organizational research and measurement methods becomes prominent. Reductionist approach

aimed toward standardization has its useful place in organizational research (Dowling, 1999).

However, standards rely on existence of universal rules. Cross-cultural employee attitudinal

research vis-à-vis organizational surveys relies on assumptions that [1] latent constructs depicted

in the survey are manifested in every culture in the same way, [2] the translated survey items are

semantically equivalent and elicit the same frame of reference in different cultural groups, and [3]

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systematic culturally-bound response bias (e.g., tendency to mark extremes on the survey response

scales) is controlled for when analyzing and interpreting the data. There are indications that a

portion of cross-cultural survey research overlooks these stipulations assuming universality of

interpretive thinking and response styles (Ryan, 1999). If erroneous, these assumptions may lead

to misleading conclusions about cross-cultural variation on dimensions of organizational

functioning, finding differences where they do not exist and overlooking important diverging

cultural tendencies (Yoo, 2002). Therefore, universality of mental models has to be verified in

every culture and language where the survey is applied proving the instrument�s measurement

equivalence in every cultural group.

Unfortunately, the use of techniques that establish equivalence of measures between

samples from different cultures remains limited to this day. However, there has been an increase

in attention paid to cross-cultural research throughout the 1980�s and 1990�s. A reasonable

expectation is that research methodology will eventually catch up with the demands of the

increasingly integrated world-wide business community.

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APPENDICES

Appendix A

Table A1. The English version of the employee opinion survey instrument.

WORK ENVIRONMENT Partly Agree/ Don’t Know/ Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 1. I feel that the amount of

work I am expected to do is reasonable. 1 2 3 4 5 6

2 KSL Desert Resorts is effectively incorporating new technology to our work environment (for example, new equipment, systems or processes). 1 2 3 4 5 6

3. Work activities are well coordinated across different work areas. 1 2 3 4 5 6

4. Adequate measures are taken at my location to ensure employee safety. 1 2 3 4 5 6

5. I have the resources I need (e.g., tools, equipment, supplies) to do my job effectively. 1 2 3 4 5 6

EMPLOYEE INVOLVEMENT Partly Agree/ Don’t Know/ Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

6. KSL Desert Resorts does a good job of:

a. Seeking the opinions and suggestions of employees 1 2 3 4 5 6

b. Acting on the suggestions of employees 1 2 3 4 5 6

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7. I feel encouraged to come up with new and better ways of doing things. 1 2 3 4 5

Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

8. Employees feel free to voice their opinions openly at XYZ Company. 1 2 3 4 5 6

9. I have the decision making authority I need to meet the needs of my customers. 1 2 3 4 5 6

10. I am satisfied with my involvement in decisions that affect me. 1 2 3 4 5 6

11. I feel I have enough say in how my job gets done. 1 2 3 4 5 6

COMMUNICATIONS Partly Agree/ Don't Know Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 12. I understand the following:

a. XYZ Company� mission 1 2 3 4 5 6

b. XYZ Company� vision 1 2 3 4 5 6

c. XYZ Company� culture 1 2 3 4 5 6

13. Communication across depart-ment lines is good. 1 2 3 4 5 6

14. XYZ Company is doing a good job of providing information on the following:

a. The way your pay is determined 1 2 3 4 5 6

b. Your benefits 1 2 3 4 5 6

ow/

/

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Partly Agree/ Don’t Know/ Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

c. Personnel policies and procedures 1 2 3 4 5 6

d. Organization changes 1 2 3 4 5 6

e. Financial performance of XYZ Company 1 2 3 4 5 6

QUALITY ORIENTATION Partly Agree/ Don’t Know/ Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 15. We are continually improving the

quality of our services. 1 2 3 4 5 6

16. Providing quality services gets higher priority than keeping costs down. 1 2 3 4 5 6

17. Where I work, day-to-day decisions demonstrate that quality is a top priority. 1 2 3 4 5 6

Don’t Kno Very Very Not Good Good Average Poor Poor Applicable 18. How would you rate the quality

of work in your work area? 1 2 3 4 5 6

w/

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CUSTOMER FOCUS Note: The word “Customer” refers to either internal customers (i.e., other employees) or external customers, whichever applies to your situation. Partly Agree/ Don't Know Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

19. Where I work, customer concerns get resolved quickly. 1 2 3 4 5 6

20. In my work area, we get enough information on how well we are meeting customer requirements. 1 2 3 4 5 6

21. XYZ Company does a good job of recognizing employee contributions to improving customer service. 1 2 3 4 5 6

Don't Kno Very Very Not Good Good Average Poor Poor Applicable 22. How would you rate the level of

customer service provided by your work area? 1 2 3 4 5 6

PERFORMANCE MANAGEMENT Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

23. I understand the measures used to evaluate my job performance. 1 2 3 4 5 6

24. I think my performance on the job is evaluated fairly. 1 2 3 4 5 6

/

w/

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25. My performance reviews are conducted on a regular and timely basis. 1 2 3 4 5 6

26. My performance reviews have been useful in helping me improve my job performance. 1 2 3 4 5 6

27. XYZ Company does a good job of:

a. Recognizing good performance 1 2 3 4 5 6

b. Dealing with poor performance 1 2 3 4 5 6

TEAMWORK Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

28. In my work area we are encouraged to work as a team to solve problems. 1 2 3 4 5 6

29. People within my work group cooperate to get the job done. 1 2 3 4 5 6

30. There is good cooperation across departments at XYZ Company. 1 2 3 4 5 6

ow/

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SUPERVISION Don’t Kno Very Very Not Good Good Average Poor Poor Applicable 31. Please rate your immediate

supervisor on each of the following:

a. Dealing fairly with everyone 1 2 3 4 5 6

b. Encouraging suggestions from employees 1 2 3 4 5 6

c. Giving you regular feedback on your performance 1 2 3 4 5 6

d. Clearly communicating goals or assignments 1 2 3 4 5 6

e. Encouraging teamwork 1 2 3 4 5 6

f. Keeping employees informed about management actions or decisions 1 2 3 4 5 6

g. Motivating employees to do their best 1 2 3 4 5 6

w/

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LEADERSHIP Note: “Senior Management” refers to Directors and Above at XYZ Company


32. Senior management is doing a good job of handling the following aspects of the business:

a. Controlling costs 1 2 3 4 5 6

b. Making the changes XYZ Company necessary to compete effectively 1 2 3 4 5 6

c. Behaving consistently with XYZ Company values 1 2 3 4 5 6

d. Exploring opportunities in new programs and services 1 2 3 4 5 6

33. Senior management does a good job of explaining the reasons behind decisions to employees. 1 2 3 4 5 6

34. Senior management gives me a clear picture of the direction in which the company is headed. 1 2 3 4 5 6

35. Senior management shows genuine interest in the well being of employees. 1 2 3 4 5 6

36. In my opinion, XYZ Company is well managed. 1 2 3 4 5 6

ow/

RECOGNITION AND REWARDS Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 37. Overall, I think I am paid fairly

compared with:

a. Other people in XYZ Company who hold similar jobs 1 2 3 4 5 6

b. People in other organizations who hold similar jobs 1 2 3 4 5 6

38. There is a clear link between job performance and pay at XYZ Company. 1 2 3 4 5 6

39. I am satisfied with the recognition I receive for doing a good job. 1 2 3 4 5 6

Partly Satisfied/ Very Don’t Know/ Very Partly Dis- Dis- Dis- Not Satisfied Satisfied satisfied satisfied satisfied Applicable 40. Overall, for the work you do,

please rate how satisfied you are with your pay. 1 2 3 4 5 6

ow/

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BENEFITS Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 41. The amount I pay for my health

care benefits is reasonable. 1 2 3 4 5 6

42. XYZ Company policies and programs help employees balance my work and nonwork commitments. 1 2 3 4 5 6

Satisfied/ Very Don’t Know/ Very Partly Dis- Dis- Dis- Not Satisfied Satisfied satisfied satisfied satisfied Applicable 43. Please rate how satisfied you are

with each of the following benefits:

a. Medical plan 1 2 3 4 5 6 b. Dental benefits 1 2 3 4 5 6 c. 401(k) plan 1 2 3 4 5 6 d. Life insurance 1 2 3 4 5 6 e. Long-term disability 1 2 3 4 5 6 f. Sick leave 1 2 3 4 5 6 g. Vacation 1 2 3 4 5 6 h. Employee meals 1 2 3 4 5 6

44. Overall, for the work you do,

please rate how satisfied you are with your benefits. 1 2 3 4 5 6

Far Far Don’t Know/ Above Above Below Below No Average Average Average Average Average Opinion 45. How do you think your total

benefits package compares with the benefits offered by other organizations? 1 2 3 4 5 6

ow/

CAREER DEVELOPMENT AND TRAINING Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 46. XYZ Company does a good job

of:

a. Providing information on career opportunities 1 2 3 4 5 6

b. Providing opportunities for career growth/development 1 2 3 4 5 6

c. Promoting the most compe-tent people 1 2 3 4 5 6

47. XYZ Company has done a good job of:

a. Providing the training I�ve needed to do my job well 1 2 3 4 5 6

b. Training employees to do other jobs in their work area or department 1 2 3 4 5 6

48. I am given the chance at XYZ Company to learn new skills. 1 2 3 4 5 6

JOB CONTENT AND SATISFACTION Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable

49. I have a clear understanding of my job responsibilities. 1 2 3 4 5 6

50. My work gives me a feeling of personal accomplishment. 1 2 3 4 5 6

51. My job makes good use of my skills and abilities. 1 2 3 4 5 6

ow/

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52. Considering everything, I am satisfied with my job. 1 2 3 4 5 6

COMPANY IMAGE AND COMMITMENT Partly Agree/ Don’t Kn Strongly Partly Strongly Not Agree Agree Disagree Disagree Disagree Applicable 53. I would recommend XYZ

Company to others as a good place to work. 1 2 3 4 5 6

54. XYZ Company provides job security to employees who perform well. 1 2 3 4 5 6

55. I would prefer to remain with XYZ Company even if a comparable job were available in another organization. 1 2 3 4 5 6

56. I am proud to say that I work for XYZ Company. 1 2 3 4 5 6

57. Considering everything, I am

satisfied with XYZ Company. 1 2 3 4 5 6

58. I feel valued as an employee of XYZ Company. 1 2 3 4 5 6

Far Far Don’t Know/ Above Above Below Below No Average Average Average Average Average Opinion

59. Compared to other resorts that I am familiar with, the overall work experience at XYZ Company is: 1 2 3 4 5 6

ow/

ow/

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BACKGROUND INFORMATION The following information will help in the survey analysis by comparing major groups. No individual can be identified. To ensure confidentiality, no group will be reported if it has less than five respondents. Please circle ONE response in each category below: I. My length of service at XYZ

Company is: 1 Less than one year

2 One year but less than three years

3 Three years but less than five years

4 Five years or more

II. My job category is: 1 Senior Management

2 Department Manager

3 Supervisor

4 Professional/Technical (non-supervisory

5 Office/Clerical

6 Other non-supervisory employee

III. My job status is: 1 Full Time

2 Part-time

IV. I currently work at another resort: 1 Yes

2 No, but have previously worked at anoth

3 No, have never worked at another resort

V. I am paid: 1 Hourly

2 Salaried

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VI. My job function is:

1 Accounting/Finance

2 Communications/Marketing

3 Facilities Maintenance 4 Grounds Maintenance 5 Golf Course Maintenance 6 Food Service 7 Human Resources 8 Housekeeping 9 Guest Services 10 Operations 11 Other Department:_______________

VII. My department code is: ____ ____ (please input the 2-digit number corresponding to your department on the Department Code Sheet that is included with this survey.)

VIII. I completed and returned the Employee

Opinion Survey conducted in 1998: 1 Yes

2 No

3 Not Applicable (i.e., did not work her

IX. I received feedback from the Employee

Opinion Survey conducted in 1998: 1 Yes

2 No

3 Not Applicable

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Table A2. The Spanish version of the employee opinion survey instrument.

ÁREA DE TRABAJO Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 1. Siento que es razonable la

cantidad de trabajo que se espera que yo realice. 1 2 3 4 5 6

2 XYZ Company está introduciendo eficazmente nueva tecnologia al ambiente de trabajo (por ejemplo, nuevo equipo, sistemas o procesos). 1 2 3 4 5 6

3. Las actividades del trabajo se coordinan bien a través de diversas áreas de trabajo. 1 2 3 4 5 6

4. En mi área de trabajo se toman las medidas adequadas para asegurar la seguridad de los empleados 1 2 3 4 5 6

5. Tengo los recursos que necesito (por ejemplo, herramientas, equipo, artículos) para hacer mi trabajo con eficacia. 1 2 3 4 5 6

TOMANDO EN CUENTA AL EMPLEADO Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

6. XYZ Company hace un buen trabajo de:

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Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 7. Siento que me animan

para pensar de nuevas y mejores maneras de hacer las cosas. 1 2 3 4 5 6

8. En XYZ Company los empleados se sienten libres de hacer saber sus opiniones abiertamente. 1 2 3 4 5 6

9. Tengo autoridad de tomar las decisiones necesarias para satisfacer las necesidades de mis clientes. 1 2 3 4 5 6

10. Me siento contento con mi nivel de participación en decisiones que me afectan. 1 2 3 4 5 6

11. Siento que tengo suficiente control sobre cómo realizar mi trabajo. 1 2 3 4 5 6

COMUNICACIÓN Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 12. Yo entiendo lo siguiente:

a. La misión de XYZ Company 1 2 3 4 5 6

b. La visión de XYZ Company 1 2 3 4 5 6

c. La cultura de XYZ Company 1 2 3 4 5 6

13. Hay buena comunicación entre departamentos. 1 2 3 4 5 6

Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 14. XYZ Company hace un

buen trabajo de proveer información sobre lo siguiente:

a. La manera de determinar mi pago 1 2 3 4 5 6

b. Mis beneficios 1 2 3 4 5 6 c. Procesos y reglas del

personal 1 2 3 4 5 6

d. Cambios en la organización 1 2 3 4 5 6

e. Rendimiento económico de XYZ Company 1 2 3 4 5 6

ORIENTACIÓN HACIA LA CALIDAD Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 15. Estamos mejorando

continuamente la calidad de nuestros servicios. 1 2 3 4 5 6

16. El proveer servicios de calidad recibe más alta prioridad que el mantener bajos costos. 1 2 3 4 5 6

17. En donde yo trabajo, las decisiones cotidianas demuestran que la calidad es de prioridad. 1 2 3 4 5 6

No Sé o Muy Muy No se

87

ENFOQUE EN EL CLIENTE Nótese: La palabra “Cliente” puede referirse tanto a los clientes internos (por ejemplo, otros empleados) como a los clientes externos, segúm lo que se aplique a su situación. Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

19. En donde yo trabajo, asuntos del cliente se resuelven rápidamente. 1 2 3 4 5 6

20. En mi área de trabajo, recibimos suficiente información acerca de cómo estamos cumpliendo con los requisitos de los clientes. 1 2 3 4 5 6

21. XYZ Company hace un buen trabajo de reconocer las contribuciones de los empleados para mejorar el servicio al cliente. 1 2 3 4 5 6

No Sé o Muy Muy No se Bueno Bueno Promedio Malo Malo Aplica 22. ¿Cómo califica Ud. el

nivel del servicio al cliente provisto por su área de trabajo? 1 2 3 4 5 6

MANEJO DEL RENDIMIENTO Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

23. Entiendo las medidas que se usan para evaluar el redimiento de mi trabajo. 1 2 3 4 5 6

88

Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

25. Las revisiones de mi redimiento se llevan a cabo de manera regular y oportuna. 1 2 3 4 5 6

26. Las revisiones de mi redimiento me han sido útiles para ayudarme a mejor mi trabajo. 1 2 3 4 5 6

27. XYZ Company hace un buen trabajo de:

a. Reconocer buen trabajo 1 2 3 4 5 6

b. Tratar con un mal desempeño de trabajo 1 2 3 4 5 6

TRABAJAR EN EQUIPO Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

28. En mi área de trabajo nos animan a trabajar en equipo para resolver los problemas. 1 2 3 4 5 6

29. Los compañeros de trabajo cooperan para realizar el trabajo. 1 2 3 4 5 6

30. En XYZ Company hay buena cooperación entre los departamentos. 1 2 3 4 5 6

89

LA SUPERVISIÓN No Sé o Muy Muy No se Bueno Bueno Promedio Malo Malo Aplica 31. Favor de calificar a su

supervisor con respecto a los siguiente:

a. Trato justo para todos 1 2 3 4 5 6

b. Pide las sugerencias de los empleados 1 2 3 4 5 6

c. Le da información regular acerca de su rendimiento 1 2 3 4 5 6

d. Comunica claramente las metas y asignaciones 1 2 3 4 5 6

e. Fomenta el trabajo en equipo 1 2 3 4 5 6

f. Mantiene a los empleados informados acerca de las acciones o deciciones de la gerencia 1 2 3 4 5 6

g. Motiva a los empleados a trabajar mejor 1 2 3 4 5 6

90

91

LIDERAZGO Nótese: "Alta Gerencia" se refiere a los miembros de la mesa ejecutiva de XYZ Company. Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

32. La Gerencia Alta hace un buen trabajo de manejar los siguientes aspectos del negocio:

a. Controlar los costos 1 2 3 4 5 6

b. Hacer los cambios necesarios para que XYZ Company competa eficazemente 1 2 3 4 5 6

c. Actuar consistentamente con los valores de XYZ Company 1 2 3 4 5 6

d. Explorar las oportunidades de nuevos programas y servicios 1 2 3 4 5 6

33. La Gerencia Alta hace un buen trabajo de explicar los motivos de las decisiones a los empleados. 1 2 3 4 5 6

34. La Gerencia Alta da información clara acerca de la dirección hacia adónde va la compañía. 1 2 3 4 5 6

35. La Gerencia Alta demuestra interés genuina en el bienestar de los empleados. 1 2 3 4 5 6

RECONOCIMIENTO Y RECOMPENSA Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 37. Tomando todo en cuenta,

creo que me pagan justamente en comparación con:

a. Otras personas en XYZ Company quienes tienen trabajo parecido 1 2 3 4 5 6

b. Personas en otras organizaciones quienes tienen trabajo parecido 1 2 3 4 5 6

38. En XYZ Company hay una conexión clara entre el rendimiento del trabajo y el pago. 1 2 3 4 5 6

39. Estoy contento con el reconocimiento que recibo por hacer un buen trabajo. 1 2 3 4 5 6

Parte Contento/ No Sé/ Muy Parte Muy No se Contento Contento Incontento Incontento Incontento Aplica 40. Tomando todo en cuenta,

por el trabajo que Ud. hace, favor de calificar qué tan contento se siente con su salario. 1 2 3 4 5 6

92

93

BENEFICIOS Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 41. La cantidad que pago por

mis beneficios de cuidado de salud es razonable. 1 2 3 4 5 6

42. Los programas y reglas de XYZ Company ayudan a los empleados a balancear los compromisos del trabajo y los de fuera del trabajo. 1 2 3 4 5 6

Contento/ No Sé/ Muy Parte Muy No se Contento Contento Incontento Incontento Incontento Aplica 43. Favor de calificar qué tan

contento se siente con los siguientes beneficios:

a. Plan de cuidado de salud 1 2 3 4 5 6

b. Plan dental 1 2 3 4 5 6 c. 401(k) plan de

jubilación 1 2 3 4 5 6 d. Seguro de vida 1 2 3 4 5 6 e. Incapacidad de largo

plazo 1 2 3 4 5 6 f. Tiempo de

efermedad 1 2 3 4 5 6 g. Vacaciones 1 2 3 4 5 6 h. Comidas para los

empleados 1 2 3 4 5 6

44. Tomando todo en cuenta,

por el trabajo que Ud. hace, favor de calificar qué tan contento se siente con sus beneficios. 1 2 3 4 5 6

Mucho Mucho No sé/

DESARROLLO DE CARRERA Y ENTRENAMIENTO Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 46. XYZ Company hace un

buen trabajo de:

a. Proveer información de oportunidades de carrera 1 2 3 4 5 6

b. Proveer oportunidades para mejorar o desarrollar la carrera 1 2 3 4 5 6

c. Dar promociones a la gente más competente 1 2 3 4 5 6

47. XYZ Company ha hecho un buen trabajo de:

a. Proveer entrenamiento que he necesitado para desempeñar bien mi trabajo 1 2 3 4 5 6

b. Entrenar a los empleados para hacer otros trabajos en su área de trabajo o departamento 1 2 3 4 5 6

48. En XYZ Company se me da la oportunidad de aprender nuevos talentos o habilidades. 1 2 3 4 5 6

CONTENIDO DEL TRABAJO Y SATISFACCIÓN Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

94

Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica

51. Mi trabajo utiliza bien mis talentos y habilidades. 1 2 3 4 5 6

52. Tomando todo en cuenta, estoy contento con mi trabajo. 1 2 3 4 5 6

IMAGEN DE LA EMPRESA Y LEALTAD Parte en Acuerdo/ No Sé o Fuertamente En Parte en En Fuertamente en No se En Acuerdo Acuerdo Desacuerdo Desacuerdo Desacuerdo Aplica 53. Yo recomendaría XYZ

Company a otros como un buen lugar en donde trabajar. 1 2 3 4 5 6

54. XYZ Company da seguridad del trabajo a los empleados que trabajen bien. 1 2 3 4 5 6

55. Yo prefería quedarme con XYZ Company aún si un trabajo parecido estuviera disponible en otra organización. 1 2 3 4 5 6

56. Me siento orgulloso de decir que trabajo en XYZ Company. 1 2 3 4 5 6

57. Tomando todo en cuenta,

estoy contento con XYZ Company. 1 2 3 4 5 6

58. Yo siento que me valoran como empleado en XYZ Company. 1 2 3 4 5 6

Mucho Mucho No sé/ Más que Más que Menos que Menos que Sin

95

DATOS PERSONALES La siguiente información ayudará en el análisis de la encuesta al comparar los groupos principales. Ningún individuo puede ser identificado. Para asegurar la confidencialidad, no se reportará ningún grupo si contiene menos que cinco respondientes. Favor de poner círculo en UNA SOLA respuesta de cada categoría abajo: I. Mi tiempo de servicio en

XYZ Company es: 1 Menos de un año

2 Un año pero menos de tres años

3 Tres años pero menos de cinco años

4 Cinco años o más

II. La categoria de mi trabajo es: 1 Gerencia Alta

2 Gerente de Departamento

3 Supervisor

4 Profesional/Técnico (no-supervisor)

5 Oficina/Administrativo

6 Otro empleado que no sea supervisor

III. Mi estado de trabajo es: 1 Tiempo Completo

2 Tiempo Parcial

IV. Actualmente trabajo en otro resort: 1 Sí

2 No, pero he trabajado previamente en ot

3 No, nunca he trabajado en otro resort

96

V.Me pagan: 1 Por Hora

2 Por Salario

VI. La función de mi trabajo es:

1 Contabilidad/Finanza

2 Comunicaciones/Comercializ

3 Mantenimiento de Edificios 4 Mantenimiento de Terrenos 5 Mantenimiento del Curso de 6 Servicio de Comida 7 Recursos Humanos 8 Servicio de Camarero 9 Servicio al Huésped 10 Operaciones 11 Otro Departamento:________

VII. El código de mi departamento es: ____ ____ (favor de escribir el número de dos dígitos que corresponda a su departamento que se encuentra en la hoja de códigos de departamentos que está incluída con esta encuesta.)

VIII. Yo completé la Encuesta de las

Opiniones de los Empleados conducída en el 1998: 1 Si

2 No

3 No Aplíca (no trabajé aquí

IX. Yo recibí información sobre la Encuesta de las Opiniones de los Empleados conducida en el 1998: 1 Si

2 No

97

Appendix B

All tables in Appendix B use shading to illustrate item clustering around the emerged factors. That is the primary purpose of the tables in Appendix B.

Table B1. Factorial Structure of the English version of the survey. Pairwise deletion.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Q1 0.084674 0.154596 0.110173 0.191082 0.128068 0.032727 0.17218 0.12243 0.181796 0.085494 0.257903 0.284061 -0.02803 0.102901 0.394678 -0.030172 -0.315188Q2 0.20776 0.100357 0.052356 0.020215 0.155799 0.163427 0.085956 0.174162 0.04889 0.106579 0.090663 0.025882 0.084536 0.127771 0.70711 0.034026 0.112673Q3 0.122902 0.174916 0.093367 0.264239 -0.018979 0.130299 0.12264 0.42704 0.13155 0.017509 0.296678 0.084278 0.088924 0.080972 0.41482 -0.090944 -0.006656Q4 0.182628 0.239721 0.093359 0.313359 0.134042 0.020535 -0.037287 0.186875 0.098362 -0.006234 -0.226602 0.034908 0.113498 -0.058419 0.469129 0.139559 -0.155571Q5 0.07136 0.179748 0.082918 0.170607 0.092732 0.129926 0.041126 0.067609 0.122042 0.060929 0.073075 0.024121 0.096705 0.06549 0.696525 0.193965 0.003173Q6A 0.286904 0.219786 0.146294 0.054916 0.179616 0.139519 0.055477 0.686473 0.042245 0.059784 0.06621 0.002713 0.157368 0.03853 0.086281 0.061126 -0.027392Q6B 0.283856 0.187548 0.135351 0.019605 0.150316 0.182791 0.086811 0.710723 0.043979 0.033922 0.134382 0.012383 0.175164 0.083558 0.149115 0.066274 0.023134Q7 0.263104 0.211649 0.033039 0.144447 0.014389 0.151852 0.018174 0.548086 0.148518 0.076293 0.05662 0.198094 0.129192 0.091881 0.203649 0.251413 -0.07739Q8 0.236846 0.20069 0.037991 0.07637 0.178491 0.15507 0.108251 0.57513 0.123704 0.073451 0.11416 0.015827 0.086221 0.164921 0.129343 0.296171 0.080493Q9 0.056211 0.129995 0.057255 0.405424 0.014388 0.189936 0.081947 0.154031 0.19658 0.058283 0.104959 0.15145 0.075098 0.109887 0.138239 0.521991 -0.102674Q10 0.244821 0.247437 0.116125 0.242121 0.111951 0.128481 0.150047 0.256047 0.034551 0.087737 0.130976 0.179335 0.084011 0.058979 0.153587 0.569335 0.041778Q11 0.175936 0.269998 0.102694 0.17545 0.03081 0.15713 0.072163 0.205086 0.177916 0.094648 0.095455 0.221079 0.077682 0.14588 0.256916 0.52967 -0.022844Q12A 0.148334 0.046934 0.108045 0.092802 0.146319 0.046022 0.045825 0.080885 0.868184 0.081215 0.026349 0.071581 0.083026 0.013162 0.050878 0.035169 0.000525Q12B 0.168885 0.069458 0.089591 0.109552 0.123075 0.065238 0.01621 0.059242 0.900425 0.110842 -0.015551 0.066922 0.111761 0.054695 0.070777 0.066056 -0.004365Q12C 0.190649 0.059761 0.15203 0.049997 0.075299 0.068347 0.020268 0.071065 0.8576 0.069133 0.060185 0.035306 0.115376 0.092071 0.109926 0.066511 -0.005086Q13 0.116227 0.177843 0.12273 0.154134 0.088715 0.178979 0.094076 0.28674 0.128952 -0.048561 0.540416 0.050075 0.220912 0.037184 0.142363 0.027534 0.051386Q14A 0.241573 0.006984 0.166878 0.008101 0.134203 0.067117 0.3585 0.072803 0.123155 0.052798 0.066283 0.044101 0.542654 0.053594 0.02506 0.237102 0.083058Q14B 0.203659 -0.026502 0.386814 0.098086 0.098982 0.006806 0.060345 0.118087 0.121394 0.101705 -0.015691 0.051356 0.667733 0.04757 0.094408 0.160221 -0.001576Q14C 0.148692 0.099395 0.170934 0.059116 0.106937 0.163126 0.087599 0.261979 0.193224 0.155845 0.179351 0.091372 0.577066 0.108786 0.065942 -0.038475 -0.158441Q14D 0.189191 0.120551 0.113468 0.100491 0.239272 0.14673 0.044307 0.227735 0.127394 0.203568 0.253327 0.080674 0.554561 0.151041 0.180787 -0.073904 0.001779Q14E 0.150257 0.125239 0.146435 0.109698 0.218478 0.271116 0.10787 0.30079 0.174184 0.105128 0.226519 0.118756 0.412705 0.042344 0.114733 -0.060003 0.009402Q15 0.282888 0.081882 0.109903 0.2454 0.217231 0.088047 0.088288 0.301482 -0.068817 -0.040685 0.340811 0.209566 0.070639 -0.023649 0.222338 0.056085 0.007786Q16 0.283566 -0.059378 0.147612 0.076424 0.227191 -0.005236 0.050138 0.072538 -0.066112 0.078469 0.562888 -0.015782 0.180352 0.095322 0.076272 0.28663 -0.001022Q17 0.288798 0.159036 0.095984 0.25835 0.24498 -0.029277 0.016957 0.12417 -0.069619 0.006393 0.397123 0.079162 -0.019023 0.220033 0.262044 0.215179 -0.090502Q18 0.137361 0.280066 0.046541 0.573993 0.133907 -0.096977 0.033796 0.081927 0.009112 0.029049 0.011717 0.266995 -0.054862 0.027961 0.205577 0.084363 -0.001129Q19 0.155406 0.091869 0.056137 0.683819 0.145984 0.114592 0.060818 -0.000304 0.080303 0.047011 0.220411 -0.032181 0.034025 0.067586 0.017186 0.09079 -0.17112Q20 0.164029 0.197076 0.035529 0.601833 0.168684 0.17662 0.083286 0.116423 0.080411 0.079981 0.066907 0.103215 0.164506 0.037012 0.028595 0.05741 -0.149547Q21 0.381729 0.119299 0.13616 0.299379 0.217406 0.18255 0.179902 0.373396 0.027483 0.082021 0.222548 -0.009764 0.130486 0.197566 -0.050272 0.147373 0.102882Q22 0.147705 0.139695 0.025855 0.695778 0.151966 -0.003228 -0.001783 0.013502 0.019815 0.000421 0.009975 0.125302 0.084771 0.105998 0.101511 0.040477 -0.007134Cus

tomer

Focus

Perform

ance

Manag

emen

t

Work

Enviro

nmen

t

Employe

e Inv

olvem

ent

Commun

icatio

ns

Quality

Focus

Q23 0.082535 0.255489 0.191685 0.151356 0.105036 0.15422 0.097953 0.107427 0.125813 -0.02131 0.1311 0.175612 0.131026 0.626938 0.079545 0.080703 0.083834Q24 0.165106 0.294417 0.141957 0.198876 0.070273 0.100838 0.180847 0.144036 0.10267 0.093145 0.013088 0.174384 0.026872 0.679517 0.043781 0.112023 0.088269Q25 0.214083 0.295707 0.075873 0.10224 0.173738 0.218111 0.233599 0.072762 -0.038023 0.051733 0.05966 0.007667 0.056784 0.54637 0.049567 0.044834 -0.304111Q26 0.296533 0.321998 0.092197 0.140856 0.09375 0.087872 0.126981 0.055213 0.061003 0.11292 0.078531 0.085329 0.106859 0.60364 0.193991 0.022545 -0.092886Q27A 0.416947 0.143315 0.09158 0.172669 0.223926 0.155691 0.260931 0.303937 0.092539 0.169968 0.165724 0.044889 0.154367 0.33829 -0.003887 0.128409 0.043102Q27B 0.16379 0.100543 0.056713 0.24597 0.225051 0.076944 0.067133 0.2837 0.113437 0.246551 0.448094 -0.068694 0.012186 0.170918 -0.11701 -0.004206 -0.060721

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.08338 0.323514 -0.020906 0.590218 0.034608 0.165647 0.119138 0.023686 0.01358 0.054324 0.074533 0.222519 0.051879 0.146103 0.095591 0.057866 0.27776Q29 -0.007044 0.265032 0.069 0.638602 -0.02186 0.163681 0.130995 0.111019 0.134903 0.04092 0.044636 0.042926 -0.076865 0.108836 0.057463 0.058716 0.205519Q30 0.344832 0.169664 0.161831 0.356134 0.197801 0.166856 0.086886 0.164344 0.039858 -0.074139 0.414347 -0.020568 0.119743 -0.019047 0.002752 0.004796 0.244981Tea

mwork

Superv

ision

Q31A 0.153282 0.768264 0.09189 0.062089 0.059861 -0.019155 0.089583 0.139595 0.052672 0.046515 0.033604 0.141999 -0.056607 0.197558 -0.008217 0.067436 -0.014007Q31B 0.13821 0.82365 0.038898 0.096949 0.111042 0.049113 0.039588 0.143481 0.013401 0.08777 -0.053455 0.096023 0.034185 0.099938 0.07029 0.123822 0.019417Q31C 0.161183 0.800312 0.024999 0.076644 0.021123 0.160553 0.073394 0.117618 0.004566 0.028129 0.015143 0.024006 0.090417 0.205498 0.041776 0.069758 -0.004502Q31D 0.137812 0.800532 0.081995 0.163473 0.05844 0.119621 0.073275 0.121665 0.021522 0.101907 0.171494 0.120849 0.057014 0.0302 0.144431 0.01247 -0.051305Q31E 0.094622 0.769645 0.048763 0.310392 0.013606 0.129883 0.083457 0.057855 0.127795 0.068442 0.055313 0.114188 -0.05786 0.067719 0.124778 -0.042881 0.093408Q31F 0.114905 0.754769 0.121655 0.189031 0.150268 0.154137 0.097831 0.034031 -0.001877 0.027421 0.097705 0.068365 0.101898 0.009194 0.138649 0.090635 -0.018797Q31G 0.186527 0.803475 0.050593 0.217253 0.122465 0.092065 0.127792 0.081757 0.068864 0.029714 0.036842 0.097451 0.046884 0.121393 0.035927 0.035287 -0.022706

Lead

ership

Superv

ision

Q32A 0.016538 0.047193 0.062341 0.173653 0.664456 0.033784 0.026327 0.080936 0.115544 0.093901 -0.10588 0.249678 0.054988 0.059771 -0.05365 -0.216966 -0.213398Q32B 0.236651 0.080383 0.141021 0.181995 0.659522 0.114685 0.12629 0.083819 0.133081 0.075995 0.187275 0.107795 0.069755 0.011931 0.186156 0.090709 -0.109034Q32C 0.267835 0.098877 0.081241 0.180767 0.66559 0.113203 0.117154 0.111815 0.084951 0.066789 0.242765 0.037328 0.064187 0.117816 0.10786 0.003067 -0.001712Q32D 0.258019 0.179999 0.063874 0.131812 0.637249 0.188657 0.113468 0.136196 0.136245 0.127069 0.080203 0.080351 0.170393 0.065225 0.112387 0.154655 0.048346Q33 0.337598 0.210767 0.139395 0.070867 0.468685 0.20785 0.057676 0.205093 0.039417 -0.020672 0.190369 0.014109 0.133863 0.179475 0.157031 0.208755 0.216115Q34 0.266705 0.133174 0.157093 0.075825 0.517096 0.320434 0.061589 0.071873 0.12158 0.129867 0.067735 0.03711 0.153888 0.097493 0.129541 0.13998 0.217927Q35 0.412616 0.192715 0.220359 0.019118 0.452195 0.178502 0.179111 0.150325 0.051151 0.047805 0.142577 -0.055958 0.087242 0.072612 0.071051 0.227973 0.181267Q36 0.567708 0.178947 0.188664 0.106161 0.418289 0.143183 0.176827 0.168427 0.150843 0.070794 0.134053 -0.021131 0.146438 0.077639 0.075492 0.040133 0.183227

Lead

ership

Reward

s and

Recog

nition

Q37A 0.142268 0.097087 0.129936 0.06882 0.068541 0.072503 0.847781 0.04297 -0.01407 0.087089 0.014221 0.08764 0.06321 0.069505 0.049178 0.05061 -0.015972Q37B 0.154503 0.102304 0.127335 0.106952 0.078768 0.091012 0.837949 0.021416 0.064119 0.066913 0.089927 0.039221 0.10266 0.07773 0.032091 -0.001304 0.04679Q38 0.367904 0.113444 0.18496 0.097976 0.186461 0.150437 0.534526 0.158657 -0.017265 0.064841 0.117965 -0.112085 0.155835 0.107039 0.060809 0.044007 -0.001098Q39 0.322901 0.306416 0.118394 0.070007 0.183966 0.223661 0.459208 0.16092 0.01852 0.122201 0.049914 0.075795 0.039668 0.2024 0.019499 0.313276 -0.007342Q40 0.26946 0.153743 0.122521 0.048384 0.045761 0.088037 0.791232 0.072212 0.038113 0.068349 0.005742 0.095675 0.003959 0.115284 0.056744 0.037092 -0.032813Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.020974 0.013281 0.80797 0.128119 0.062237 0.06146 0.114121 0.091744 0.0473 0.05442 -0.141415 0.033438 0.029405 0.088107 0.062205 0.061042 -0.147794Q42 0.138092 0.206999 0.370631 0.13452 0.129141 0.276108 0.203281 0.176166 0.108644 0.201895 -0.00843 0.05876 0.079996 0.093837 -0.013466 0.213674 -0.314686Q43A 0.12755 0.0499 0.837375 0.075404 0.102981 0.062767 0.090994 0.094491 0.103623 0.150295 -0.002816 0.018257 0.078755 0.059326 0.012783 -0.008567 0.04901Q43B 0.106065 0.084068 0.778498 0.036859 0.086858 0.069288 0.047659 0.058028 0.076525 0.194627 0.057404 0.059048 0.137043 0.080531 0.064503 0.001597 0.014458Q43C 0.191146 0.122911 0.547349 -0.0501 0.030557 0.059049 0.02802 -0.054075 -0.005781 0.300224 0.377243 0.014348 0.293229 0.064513 0.074809 -0.004326 0.048849Q43D 0.18305 0.08438 0.501653 0.11178 0.114356 0.134526 0.131575 -0.008806 0.037809 0.596456 0.025696 0.027969 0.203004 0.107907 0.107929 -0.046555 0.083728Q43E 0.138847 0.079709 0.477666 0.032784 0.17882 0.072331 0.077612 0.019532 0.113613 0.664007 -0.034471 0.001225 0.145217 0.113573 0.170542 0.079859 0.044384Q43F 0.144705 0.085054 0.378183 0.043671 0.077901 0.09456 0.111622 0.061328 0.115807 0.708622 -0.004325 0.145074 0.090781 0.053547 0.00347 0.133608 -0.043269Q43G 0.133288 0.147028 0.333735 0.033007 0.077716 0.053395 0.084545 0.086086 0.12907 0.678897 0.094659 0.117144 0.102276 -0.001409 0.037419 0.037696 -0.05666Q43H 0.207905 0.063778 0.16648 0.11193 0.047496 0.08692 0.195022 0.240801 0.036023 0.358805 0.091648 0.077759 -0.139939 -0.047985 0.041281 -0.088584 0.362602Q44 0.202153 0.118896 0.71241 -0.030586 0.10152 0.078796 0.141675 0.049416 0.083001 0.245497 0.170039 0.143767 0.055729 0.024683 -0.01486 0.124029 0.026288Q45 0.184506 0.066873 0.72886 -0.021429 0.004131 0.101167 0.135876 0.077092 0.066377 0.075037 0.324679 0.098126 0.050331 0.021876 0.057311 0.014492 0.075353

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.133365 0.069342 0.051226 0.093027 0.152877 0.704279 0.085452 0.131344 0.077062 0.169674 -0.073994 0.09966 0.210816 0.112949 0.115687 0.003005 0.040089Q46B 0.271517 0.103342 0.090941 0.075853 0.14941 0.689796 0.151393 0.2052 0.040901 0.136543 -0.016646 0.134926 0.132682 0.150318 0.052786 0.060824 0.076878Q46C 0.35968 0.126885 0.063459 0.012189 0.233933 0.502556 0.189494 0.281803 0.032108 0.033942 0.146054 0.083502 0.071141 0.140421 0.117627 0.089174 0.100501Q47A 0.219632 0.231546 0.209036 0.214949 0.061868 0.596011 0.121499 0.02599 0.029654 0.014611 0.260355 0.159271 0.035417 0.10666 0.054667 0.159435 -0.130536Q47B 0.15601 0.317062 0.150352 0.181412 0.082955 0.626213 0.102153 0.049341 0.099542 -0.019391 0.339883 0.103833 -0.039094 0.000729 0.078713 0.099627 -0.095863Q48 0.323209 0.22368 0.162854 0.114371 0.180017 0.554048 0.056604 0.227746 0.042161 -0.011658 -0.030054 0.162061 -0.090136 0.045741 0.164516 0.078245 -0.011019

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.043748 0.233148 0.056903 0.172166 0.0344 0.219379 0.02271 -0.124453 0.124203 0.148085 0.146986 0.643456 0.035423 0.142333 0.028293 0.096451 -0.039022Q50 0.23097 0.163926 0.116346 0.199182 0.135082 0.096288 0.108565 0.11554 0.076099 0.097163 -0.023963 0.764151 0.090058 0.051183 0.063226 0.055307 0.019287Q51 0.252964 0.235372 0.170246 0.167427 0.140079 0.171642 0.031727 0.11525 0.000501 0.023433 -0.070113 0.651929 0.069031 0.12264 0.023208 0.095918 0.031964Q52 0.56417 0.242126 0.075194 0.054297 0.129325 0.165822 0.198773 0.12182 0.079071 0.036164 0.056705 0.49319 0.067311 0.12034 0.077376 0.176191 -0.012316Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 0.685342 0.180666 0.161196 0.138078 0.095868 0.077257 0.25747 0.268034 0.118526 0.087207 0.151967 0.107777 0.082297 0.09742 0.078731 0.033412 0.044082Q54 0.61025 0.117905 0.195793 0.172272 0.085714 0.12971 0.106317 0.186765 0.123263 0.251687 0.061751 0.040112 0.120212 0.098716 0.114667 0.116506 -0.172008Q55 0.665834 0.18649 0.089469 0.124173 0.112787 0.190617 0.184421 0.090482 0.105759 0.143721 0.025938 0.191985 0.117211 0.063625 0.101465 0.056553 -0.051498Q56 0.76199 0.177097 0.116739 0.118411 0.178501 0.081213 0.103041 0.151918 0.148023 0.10776 0.028191 0.093622 0.108023 0.075158 0.111069 0.004694 -0.056203Q57 0.725106 0.203689 0.127294 0.147005 0.136188 0.186664 0.194896 0.141527 0.154698 0.098239 0.146834 0.150133 0.123923 0.078717 0.053071 0.031698 0.041535Q58 0.684651 0.181125 0.135447 0.108006 0.163171 0.251051 0.160192 0.136481 0.132719 0.146193 0.126495 0.172156 0.076157 0.101901 0.091899 0.157612 0.00262Q59 0.61951 0.153307 0.265527 0.130637 0.182138 0.175191 0.178727 0.10858 0.163877 -0.025409 0.190614 0.099645 0.081202 0.153041 0.108468 -0.013473 0.054188Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Deletion: Pairwise.

Compa

nt Im

age a

nd

Commitm

ent

17

98

Table B2. Factorial Structure of the English version of the survey. Listwise deletion.

0491169038

138563 0.194307 -0.010087 0.235867 -0.08711 0.069336 -0.120291 0.045593 0.0481214053 0.084323 -0.235809 -0.15686 0.105885 0.140226 0.010925 -0.087511 -0.10967

Q5 0.16233 0.234788 0.155156 0.15197 -0.005718 0.030179 0.078688 0.167055 0.708682 0.207896 0.095039 0.033216 0.090678 0.042638 0.029582 -0.039303 -0.093447 -0.079702Q6A 0.653059 0.244753 -0.05972 0.108054 0.206803 0.074895 0.146161 0.095045 0.162543 0.072584 0.319103 -0.08765 0.012839 -0.006145 0.0499 0.108681 0.249232 -0.104933

10059170135011525

Q9 0.181272 0.206947 0.148543 0.081963 0.154391 -0.023193 0.107483 0.659021 0.258714 0.187364 0.146026 -0.043864 0.187599 0.082117 0.025833 -0.04967 0.034438 -0.053507Q10 0.412322 0.375357 0.359256 0.093078 0.12857 -0.016816 0.050484 0.3526 0.177141 0.190524 0.300827 -0.096767 0.205516 -0.03653 -0.072427 -0.232153 0.028584 0.040927Q11 0.320112 0.290448 0.399379 0.123838 0.313841 -0.01106 0.148698 0.294496 0.362743 0.055292 0.023845 -0.2294 0.05326 0.099762 -0.116869 -0.159466 -0.010339 0.034453Q12A 0.161473 0.052569 0.10176 0.050922 -0.016619 0.117208 0.879204 0.002548 0.081517 0.102434 0.104819 -0.031155 0.056171 0.050929 0.111311 -0.035773 0.001654 0.03731Q12B 0.192144 0.030312 0.15179 0.041143 0.064978 0.191525 0.881422 0.077791 0.033636 0.073416 0.077993 -0.065463 0.001324 0.104786 0.071157 0.007048 0.071779 0.003344Q12C 0.239496 0.14758 0.053254 -0.046502 0.106696 0.089851 0.848492 0.084939 0.087539 0.042423 0.068856 0.057524 -0.062348 -0.008992 -0.094759 0.06453 0.039488 0.01002Q13 0.536973 0.142017 -0.093289 0.059173 -0.027618 -0.002867 0.049818 0.151655 0.150782 0.06334 0.310405 0.444083 0.185342 0.065377 -0.037532 0.137058 0.17715 -0.006746Q14A 0.356317 -0.038001 0.116568 0.470202 0.136917 0.160641 0.07861 0.012661 0.225781 -0.031173 0.392716 0.045705 -0.052557 -0.039341 -0.006873 -0.032764 -0.092685 -0.402144Q14B 0.284658 0.061667 0.04405 0.26014 0.228561 0.279065 0.112103 0.085635 0.178061 -0.000866 0.629205 0.079329 -0.136249 0.01413 0.071052 0.055428 0.024906 -0.063634Q14C 0.216165 0.090122 -0.018941 0.096887 0.185793 0.098193 0.214729 0.201724 -0.021775 0.179662 0.664142 0.214681 0.004705 0.071167 -0.127422 -0.030724 -0.039628 0.120196Q14D 0.418705 0.081467 0.007937 0.049238 0.079164 0.255046 0.106394 0.051953 0.094986 0.036468 0.465898 0.238717 0.147043 0.303186 -0.098105 0.180466 -0.165759 0.067308Q14E 0.59241 0.058822 -0.008189 0.088531 0.084668 0.116523 0.226703 0.1211 0.121045 0.237931 0.3445 0.084798 0.149472 0.156565 0.062253 0.026911 -0.135477 0.230703Q15 0.405508 0.040526 0.172054 0.108658 0.15623 -0.114956 -0.024713 0.04665 0.158241 0.254106 0.330667 0.138165 0.17551 0.344148 0.234576 -0.082297 0.099088 -0.119028Q16 0.492662 0.149818 0.166026 -0.040331 0.135076 0.11307 -0.0813 0.192615 0.164511 -0.050289 0.185795 0.426931 -0.022186 0.218254 -0.145671 -0.006291 0.070339 -0.076807Q17 0.432368 0.342341 0.228223 -0.013133 0.156745 0.142001 -0.052266 0.173035 0.506002 -0.01103 0.110449 0.09734 0.155257 0.204936 0.052337 -0.072081 -0.012239 -0.017298Q18 0.065358 0.355577 0.355858 0.006855 0.026951 0.128962 0.002405 0.181399 0.249886 0.135702 -0.116352 -0.157816 0.163513 0.056393 0.573261 0.010994 0.10982 0.105006Q19 0.087729 0.194298 0.144201 0.146981 0.027143 0.07837 0.059193 0.72411 0.060242 0.094363 0.089269 0.186519 0.244618 0.075648 0.20145 0.119214 -0.081292 0.053941Q20 0.20395 0.144761 0.307075 0.119252 -0.0509 0.254696 0.053968 0.528319 0.210314 0.10409 0.248375 0.06442 0.159074 0.041392 0.233795 0.098633 0.037865 0.047129Q21 0.680491 0.184247 0.171617 0.137484 0.13526 0.156517 0.09429 0.16157 -0.045426 0.072233 0.18867 0.124319 0.24911 -0.059908 0.182263 -0.030021 -0.094004 -0.016918Q22 0.157221 0.141236 0.130089 0.023359 0.075764 0.124345 0.073302 0.132773 -0.056927 -0.049277 0.001281 0.051042 0.071766 0.061533 0.813357 -0.014305 -0.07033 -0.042656Cus

tomer

Focus

Perform

ance

Manag

emen

t

Employe

e Inv

olvem

Commun

icatio

ns

Quality

Focus

Q23 0.248008 0.361452 0.140719 0.149806 0.340261 0.127083 0.054845 0.201897 0.275213 -0.032341 0.142206 0.031354 0.262496 -0.180058 0.011031 0.147132 -0.306541 0.346376Q24 0.274164 0.371286 0.369106 0.180864 0.33154 0.18547 0.052342 0.116198 0.082717 0.006343 0.103739 -0.090365 0.329418 -0.224455 0.083207 0.188154 -0.131868 0.129745Q25 0.242012 0.498136 0.220938 0.14938 0.318285 0.17193 -0.026086 0.258588 -0.050661 0.116966 0.076388 -0.07042 0.157942 0.014379 -0.132971 0.35893 -0.099781 -0.053608Q26 0.312121 0.348198 0.294887 0.220401 0.263672 0.185279 -0.028542 0.090437 0.154547 0.083236 0.075907 0.008726 0.253228 -0.003084 -0.020385 0.449476 -0.156144 -0.025282Q27A 0.621013 0.282251 -0.003048 0.205019 0.162922 0.220776 0.153814 0.115963 0.022198 0.121889 0.20756 -0.054113 0.225652 0.026419 0.06958 0.088807 0.069465 0.035082Q27B 0.468311 0.22621 -0.10398 -0.073702 0.133768 0.391343 0.034067 0.112485 -0.067062 -0.039159 -0.011488 0.055535 0.376481 0.1526 0.150216 -0.043578 0.141832 -0.239842

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.148512 0.278978 0.339428 0.078321 -0.004791 0.018053 -0.039602 0.219752 0.208659 0.139412 0.010772 -0.058465 0.64029 0.127976 0.124662 0.062664 0.019796 0.078283Q29 2.55E-05 0.29885 0.120809 0.194108 0.067675 0.041236 0.004236 0.256173 0.09611 0.05361 -0.045258 0.023682 0.718509 -0.020422 0.071098 0.001087 0.162056 -0.015992Q30 0.436406 0.209601 0.167542 0.062475 -0.033566 0.045622 -0.034123 0.118497 0.006203 0.270399 0.161611 0.473876 0.218099 -0.103177 0.042673 -0.142832 0.207987 0.201533Tea

mwork

Superv

ision

Q31A 0.170708 0.803628 0.148048 0.215853 0.118235 0.030952 0.10278 0.029166 0.013079 0.011173 -0.093531 -0.029095 0.150402 -0.008945 0.042464 -0.015747 0.0155 0.069008Q31B 0.14193 0.853016 0.075625 0.110754 0.030184 0.050843 0.173622 0.0886 0.168718 0.062646 0.104232 -0.022991 -0.043404 0.035932 -0.017846 0.055973 0.006913 -0.000818Q31C 0.119481 0.839839 0.091191 0.123179 0.047094 0.02433 0.036371 0.157021 0.013703 0.046971 0.083497 0.112971 0.096661 -0.000865 -0.076566 -0.043276 -0.061376 0.070602Q31D 0.216723 0.786241 0.135934 0.087924 0.115818 0.06887 -0.039007 0.077991 0.166733 0.096064 0.105198 0.134919 0.137673 0.114264 0.160889 0.045838 0.045386 -0.081692Q31E 0.118839 0.640174 0.217776 0.094311 -0.042394 -0.010466 0.020697 0.048313 0.150589 0.282554 0.03038 0.193129 0.299298 0.029562 0.262013 0.183975 0.127855 0.16281Q31F 0.298644 0.735892 0.08547 0.086481 0.121443 0.153893 -0.015566 0.18001 0.272034 0.129101 0.051886 0.051062 -0.015481 0.091813 0.145767 -0.014326 -0.014113 -0.036235Q31G 0.224664 0.7479 0.289615 0.137874 0.092101 0.117196 0.100687 0.030322 0.067661 0.131231 0.028601 0.1662 0.073063 -0.010578 0.184274 -0.001823 -0.000561 -0.023596

Lead

ership

Superv

ision

Q32A 0.352341 0.058722 0.150973 -0.095236 0.098068 0.197618 0.036002 0.088557 -0.004305 0.057729 -0.001897 -0.134304 0.036382 0.721971 0.214078 0.078873 -0.048883 -0.013842Q32B 0.318183 0.037699 0.129365 0.239711 0.078809 0.207608 0.202723 0.031895 0.158638 0.096558 0.101226 0.201206 0.015744 0.643012 -0.086075 -0.019529 -0.016289 0.099285Q32C 0.450563 0.141277 0.080491 0.054152 0.122709 0.120736 0.211148 0.093647 0.18457 -0.002565 0.162441 0.096701 0.020671 0.394977 -0.035932 -0.064268 0.068365 0.512888Q32D 0.463528 0.222826 0.279323 0.167016 0.058176 0.232615 0.156718 0.098867 0.123706 0.131893 0.326807 -0.134277 -0.148572 0.306691 -0.034294 -0.072103 0.17706 0.212755Q33 0.795959 0.311316 0.153154 0.014482 0.156531 0.024174 -0.062068 -0.005681 0.069011 0.15884 -0.028867 -0.021607 -0.02435 0.074136 0.009174 -0.129481 -0.097315 -0.005189Q34 0.690928 0.097298 0.152833 0.030303 -0.001625 0.247904 0.082107 0.114673 0.216736 0.254799 -0.046302 0.028387 -0.03239 0.224098 0.012279 0.002034 -0.054743 0.117869Q35 0.732561 0.138402 0.174399 0.175697 0.140539 0.164998 0.155366 -0.071561 0.163754 0.047269 -0.094452 0.125157 -0.094583 0.149789 0.030537 -0.019026 -0.132988 -0.156337Q36 0.750272 0.094154 0.082108 0.218397 0.087719 0.046483 0.178033 -0.038228 0.141948 0.090789 0.089556 0.103569 -0.075174 0.24625 0.128397 0.062535 -0.022288 0.133173

Lead

ership

Reward

s and

Recog

nition

Q37A 0.238105 0.239962 0.012969 0.795462 0.046061 0.10259 -0.006679 0.122911 0.043057 0.07539 0.04321 -0.012474 0.079302 0.050668 -0.003519 -0.052687 0.06702 -0.090192Q37B 0.148404 0.126782 0.123173 0.824506 0.089586 0.111985 -0.046028 -0.037572 0.088998 0.069662 0.1316 0.109241 0.163175 0.102785 0.018094 0.020702 0.041043 0.051545Q38 0.553535 0.127598 0.12336 0.532965 0.19241 0.137404 0.009833 0.086414 0.079454 0.026164 0.11761 0.071672 -0.132184 0.073473 0.061604 0.049172 0.10117 0.005818Q39 0.376553 0.352659 0.243503 0.454079 0.175981 0.146844 0.072057 0.304077 0.085472 0.180516 0.125135 -0.085025 0.004346 0.020235 0.060617 0.052771 0.220264 0.043744Q40 0.18283 0.272539 0.045521 0.796218 0.139508 0.136052 0.074772 0.138926 0.103526 0.055445 0.025632 -0.018868 0.060948 -0.0975 0.012222 0.115013 0.04851 0.076222Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.063915 0.055664 0.104305 0.106874 0.840405 0.161577 0.066269 0.063559 0.069169 0.0774 0.049048 -0.008502 0.012917 0.023714 0.084104 -0.039641 0.021723 -0.004649Q42 0.34252 0.06945 0.154058 0.221464 0.319155 0.451413 0.069247 0.204234 -0.024161 0.249612 0.141733 0.139323 -0.177025 -0.038805 0.07784 -0.117142 -0.010428 0.038261Q43A 0.192666 0.066916 0.049333 0.156511 0.824989 0.208972 0.051133 0.023706 0.000831 0.134034 0.12162 0.083227 0.058882 0.136703 0.045186 0.041366 0.058292 0.049177Q43B 0.234056 0.148182 -0.030959 -0.03852 0.785924 0.158062 0.062601 0.072107 0.024636 0.043981 0.068058 0.109995 0.104289 -0.017763 -0.117433 0.136509 0.000469 -0.027373Q43C 0.099456 0.107232 0.006178 0.001281 0.426522 0.297422 -0.116843 0.097438 -0.04816 -0.030139 0.157337 0.553685 -0.153923 0.009205 0.01334 0.098916 0.018981 -0.07553Q43D 0.173966 0.022346 0.120081 0.103459 0.215157 0.697133 -0.10001 -0.055345 -0.056759 0.197628 0.165364 0.184387 -0.030311 0.153852 0.08546 0.265137 0.033095 0.011745Q43E 0.202361 0.126774 0.055249 0.05305 0.334343 0.739612 0.124759 -0.023969 0.190729 0.049763 0.08824 0.034042 -0.098644 0.064226 0.079961 0.105286 0.029108 0.019908Q43F 0.057746 0.069786 0.034521 0.238055 0.206366 0.802187 0.190421 0.081043 0.059795 0.146752 0.076003 -0.007828 0.11176 -0.009222 0.118525 -0.038668 0.068141 0.042857Q43G 0.059557 0.091155 0.031339 0.075022 0.119773 0.738226 0.27656 0.112986 0.096984 0.03181 0.018562 0.03625 0.13977 0.157886 -0.059229 -0.074997 0.07555 -0.011757Q43H 0.154033 -0.046362 0.112896 0.233009 0.113834 0.229814 0.129392 -0.025757 0.00994 0.004308 -0.012355 0.111725 0.173923 -0.035667 -0.043212 -0.011566 0.672714 0.039317Q44 0.144781 0.188385 0.049232 0.258691 0.580992 0.328363 0.04859 0.000382 0.094666 -0.064003 0.070848 0.308812 -0.092231 0.132622 0.166473 -0.083062 0.136215 0.019669Q45 0.088619 0.103006 0.044602 0.13228 0.544509 0.084021 -0.002594 -0.037968 0.019288 -0.039528 0.104728 0.657146 -0.005324 0.016061 0.060366 -0.06473 0.005531 0.036357

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.26273 0.080824 0.096651 0.038407 0.061178 0.238254 0.131724 0.092381 0.098628 0.804458 0.191535 -0.051723 0.106982 0.070317 0.005626 -0.033181 0.017922 0.037043Q46B 0.345021 0.202392 0.24074 0.149372 0.100702 0.272151 0.025644 0.05788 0.05356 0.660643 0.114366 -0.053552 0.13087 0.045481 0.021094 -0.068115 0.036806 -0.043983Q46C 0.62889 0.205688 0.083155 0.236221 0.07645 0.000738 0.104273 0.16709 0.164345 0.39009 -0.072084 0.00936 0.074999 0.160005 -0.028446 0.089451 0.158025 -0.006282Q47A 0.273097 0.270252 0.152684 0.133014 0.161997 0.021044 0.097472 0.456837 0.153042 0.50681 0.07876 0.240337 -0.032936 0.023074 0.032692 0.076003 -0.140395 0.01306Q47B 0.248277 0.301469 0.031979 -0.04061 0.080404 -0.009213 0.127631 0.322005 0.232166 0.43055 -0.209718 0.395448 0.042747 -0.009354 -0.112483 0.096551 -0.071122 -0.016075Q48 0.346644 0.262262 0.137324 0.099844 0.071839 0.020702 0.174605 0.176618 0.250063 0.613471 -0.015406 0.060957 -0.064105 0.054274 -4.87E-05 0.266749 0.039968 0.021613

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.071267 0.286847 0.698401 -0.022758 0.013922 0.067174 0.188904 0.323312 0.135607 0.178181 -0.031693 -0.001368 0.143412 0.057553 0.104396 -0.08293 0.029036 0.130489Q50 0.171951 0.223395 0.800297 0.129616 0.025907 0.015863 0.071058 0.070225 0.069541 0.079392 -0.026183 0.031834 0.114702 0.128007 0.254686 -0.022882 0.001541 -0.004721Q51 0.11942 0.210899 0.733946 0.053575 0.102916 0.148148 0.115136 0.072493 0.181129 0.120681 0.054591 0.18463 0.056605 0.103623 0.033517 0.180459 0.051318 -0.149095Q52 0.37153 0.409471 0.575477 0.347048 0.101185 -0.015753 0.153009 0.102573 0.022375 0.055329 0.057856 -0.027273 -0.008471 -0.016058 -0.035961 0.119684 0.05851 0.09444Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 0.593207 0.149039 0.216697 0.36357 0.200108 -0.014918 0.236614 0.072103 0.052911 0.04203 0.11945 0.164336 0.099952 0.042099 0.10205 0.195802 0.082809 0.077203Q54 0.589113 0.10637 0.156972 0.15428 0.135164 0.20926 0.237187 0.276229 0.107225 0.14737 -0.029159 0.045858 -0.093423 0.04668 0.07761 0.326036 0.124644 -0.061855Q55 0.437073 0.295375 0.369999 0.209982 0.008882 0.126996 0.187101 0.330929 -0.004396 0.197363 0.098556 -0.015869 0.005057 0.113721 0.004902 0.23514 0.168936 0.280317Q56 0.502206 0.099883 0.362212 0.198097 0.070024 0.083053 0.270485 0.23234 0.116624 0.13223 0.107722 0.027224 -0.016396 0.23202 -0.000118 0.370073 0.185068 0.12984Q57 0.595118 0.187303 0.316484 0.32834 0.045936 0.020638 0.267814 0.188087 0.000301 0.140453 0.067837 0.168823 0.214106 0.056134 -0.022342 0.144133 0.012303 0.154226Q58 0.556216 0.186441 0.318626 0.334318 0.028654 0.079226 0.311541 0.209718 0.071001 0.114044 0.031024 0.213242 0.084 0.104781 -0.079595 0.014395 0.086308 0.101163Q59 0.472459 0.196267 0.189821 0.294381 0.153987 0.008234 0.220856 -0.009588 0.040507 0.176248 0.303159 0.3391 -0.047152 0.002 0.213355 0.069609 0.10271 0.15787Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Deletion: Listwise.

Compa

nt Im

age a

nd

Commitm

ent

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Q1 0.252049 0.248624 0.271174 0.027703 0.078602 0.274035 0.012941 0.403882 0.322985 0.019855 -0.075611 -0.018999 0.021941 -0.047986 0.12039 -0.187036 0.076319 0.2Q2 0.261194 0.099525 0.073737 0.104771 0.019873 0.127118 0.11623 0.05277 0.751628 0.052456 0.04349 0.146161 0.097424 0.077599 -0.076018 0.195844 0.1381 0.1Q3 0.411915 0.250837 0.04274 0.157972 0.053015 0.070635 0.018223 0.187273 0.458362 0.Q4 0.132699 0.25581 0.228356 0.076062 0.123638 0.111201 0.280623 0.222212 0.508984 0.Work

Enviro

nmen

t

18

Q6B 0.60768 0.189956 -0.073436 0.064998 0.183612 -0.005436 0.044204 0.093538 0.25032 0.175849 0.409257 0.042071 -0.009569 -0.029706 0.088682 0.082381 0.286063 -0.Q7 0.429104 0.210101 0.269184 0.120498 0.158743 -0.012466 0.244737 0.368477 0.271046 0.091008 0.269908 -0.03779 0.141227 -0.126754 -0.095781 0.153413 0.214869 -0.Q8 0.595139 0.144499 0.019403 0.151305 0.089006 0.111585 0.198394 0.088245 0.26906 0.172052 0.350694 0.000225 0.240136 -0.097769 0.055326 -0.070569 0.036417 0.

ent

99

Table B3. Factorial Structure of the English version of the survey. Mean substitution.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Q1 0.090092 0.144948 0.098385 0.169264 0.03394 0.167108 0.160889 0.097982 0.079565 0.174221 0.246625 0.271114 -0.010754 0.12576 0.400823 0.019596 -0.323448Q2 0.202317 0.092988 0.031472 0.023349 0.165129 0.087894 0.129113 0.184927 0.121261 0.053414 0.095948 0.023929 0.079473 0.131511 0.696168 0.016596 0.102539Q3 0.11738 0.169592 0.076963 0.255023 0.126967 0.121745 -0.011486 0.416567 0.02226 0.132009 0.300963 0.085243 0.087034 0.079722 0.412406 -0.0816 -0.051526Q4 0.188632 0.231579 0.083174 0.310018 0.023268 -0.042302 0.136063 0.163222 0.015092 0.091546 -0.222124 0.021286 0.130222 -0.042639 0.477773 0.159517 -0.127998Q5 0.072289 0.176848 0.063491 0.171159 0.133359 0.042229 0.076217 0.061904 0.061757 0.121457 0.075195 0.030255 0.112233 0.063507 0.691446 0.187182 0.027873Q6A 0.288037 0.22027 0.132178 0.056021 0.143375 0.051414 0.166772 0.676433 0.059168 0.03677 0.070782 -0.007372 0.169782 0.045031 0.092554 0.079213 -0.031155Q6B 0.285777 0.189192 0.120698 0.01549 0.179745 0.083735 0.138213 0.692753 0.040584 0.043377 0.136834 0.010994 0.18929 0.074755 0.150271 0.069348 0.016131Q7 0.262698 0.210328 0.025164 0.145008 0.149841 0.011389 0.017718 0.53892 0.077088 0.143267 0.053727 0.190975 0.135028 0.092294 0.20674 0.264475 -0.077889Q8 0.231642 0.196889 0.029901 0.079934 0.159096 0.104068 0.153293 0.571463 0.058725 0.111895 0.120754 0.018126 0.106581 0.163434 0.13795 0.294111 0.107615Q9 0.060053 0.114217 0.043717 0.396091 0.176706 0.073496 0.010569 0.156194 0.056664 0.202786 0.106059 0.141457 0.06682 0.110347 0.132087 0.528751 -0.09446Q10 0.24611 0.247194 0.100438 0.234788 0.128383 0.143903 0.094176 0.259039 0.091423 0.031795 0.136809 0.180308 0.091854 0.065721 0.154382 0.560143 0.073886Q11 0.179608 0.26561 0.088984 0.16945 0.153172 0.070869 0.023698 0.203465 0.099811 0.173809 0.098154 0.225225 0.083183 0.1372 0.255463 0.532695 0.0014

Work

Enviro

nmen

t

Employe

e Inv

olvem

ent

Commun

icatio

ns

Q12A 0.14775 0.042442 0.09072 0.091329 0.047009 0.04443 0.138465 0.077705 0.07918 0.869113 0.022991 0.065468 0.086793 0.013035 0.051934 0.039381 -0.005866Q12B 0.157502 0.06543 0.07228 0.105772 0.062296 0.014871 0.1145 0.057151 0.103129 0.897886 -0.012319 0.056591 0.107429 0.050539 0.073366 0.069368 0.001729Q12C 0.190821 0.057468 0.131054 0.046691 0.069509 0.017703 0.066962 0.058271 0.07842 0.853475 0.062589 0.031894 0.126921 0.083097 0.107954 0.063023 0.003956Q13 0.119419 0.170124 0.098493 0.154752 0.174512 0.091174 0.074489 0.276007 -0.028632 0.124847 0.541892 0.052396 0.226136 0.041836 0.148001 0.01791 0.036056Q14A 0.246577 0.00658 0.125577 0.012162 0.071258 0.358128 0.096897 0.057239 0.078447 0.119357 0.069978 0.049179 0.553283 0.061882 0.035881 0.215102 0.138148Q14B 0.199067 -0.024906 0.32617 0.093412 0.009684 0.066656 0.07293 0.106235 0.129987 0.116613 -0.002154 0.057252 0.687336 0.050879 0.099873 0.152137 0.030528Q14C 0.144373 0.099777 0.135909 0.047564 0.16321 0.081706 0.120295 0.25127 0.162564 0.172256 0.173217 0.092828 0.588749 0.106339 0.059291 -0.022608 -0.164621Q14D 0.195594 0.116634 0.062425 0.096659 0.149203 0.040993 0.2209 0.215422 0.187736 0.13109 0.254577 0.077058 0.5617 0.146115 0.172581 -0.068957 -0.00286Q14E 0.155024 0.120123 0.114617 0.10057 0.272324 0.102422 0.210662 0.273741 0.118693 0.162714 0.233781 0.105929 0.423152 0.031145 0.117163 -0.061146 -0.02104

Commun

icatio

ns

Quality

Focus

Q15 0.278444 0.079583 0.113103 0.241096 0.093146 0.09082 0.201893 0.294593 -0.039155 -0.069615 0.335394 0.206009 0.079489 -0.020816 0.227081 0.049698 0.013744Q16 0.284661 -0.059887 0.118668 0.074769 0.007739 0.052814 0.201321 0.067769 0.078366 -0.06086 0.565324 -0.014523 0.183796 0.084604 0.077154 0.283877 0.027655Q17 0.290293 0.153374 0.087431 0.255021 -0.022499 0.016653 0.215978 0.119207 0.013005 -0.070327 0.398408 0.076002 -0.004775 0.219774 0.268059 0.226855 -0.068079Q18 0.137597 0.271742 0.044719 0.568593 -0.087738 0.035452 0.135309 0.069856 0.021905 0.010858 0.011588 0.262561 -0.046485 0.028725 0.221086 0.082624 -0.005934

Custom

er

Focus

Quality

Focus

Q19 0.150016 0.087804 0.046898 0.674684 0.107456 0.060839 0.156833 0.005318 0.045836 0.080301 0.217812 -0.033832 0.026166 0.071535 0.006633 0.123883 -0.171651Q20 0.165606 0.190968 0.023823 0.592553 0.172743 0.079984 0.17069 0.108089 0.069923 0.070967 0.071979 0.09316 0.164941 0.037829 0.027755 0.083709 -0.158252Q21 0.380023 0.11633 0.111512 0.30117 0.185754 0.180186 0.190931 0.371776 0.083877 0.02369 0.231917 -0.003394 0.14664 0.199369 -0.048088 0.144003 0.109039Q22 0.153314 0.136772 0.030499 0.688506 0.001113 -0.008365 0.143522 -0.005828 -0.005852 0.013446 0.007967 0.118898 0.100579 0.102127 0.11081 0.040783 0.014668Cus

tomer

Focus

Perform

ance

Manag

emen

tQ23 0.073639 0.254045 0.18408 0.152652 0.157284 0.104868 0.087821 0.106927 0.00229 0.125717 0.135394 0.191047 0.131235 0.603871 0.072942 0.074958 0.115395Q24 0.148325 0.284306 0.120483 0.197146 0.102567 0.185837 0.059162 0.145579 0.108042 0.091147 0.017628 0.191149 0.029504 0.66957 0.038711 0.100918 0.113077Q25 0.218987 0.272687 0.064901 0.086736 0.213016 0.205873 0.170419 0.041082 0.041785 -0.038167 0.055402 -0.010768 0.063413 0.576925 0.057832 0.077871 -0.261871Q26 0.290199 0.290665 0.06825 0.130204 0.082948 0.102296 0.076046 0.050786 0.114275 0.059448 0.073184 0.067478 0.101991 0.631632 0.192674 0.02029 -0.077863Q27A 0.415375 0.138989 0.060669 0.166553 0.150379 0.255678 0.196048 0.302801 0.173631 0.083004 0.177078 0.044863 0.161933 0.349159 -0.005969 0.125662 0.054054Q27B 0.179476 0.092549 0.024462 0.234304 0.080618 0.058877 0.197061 0.268844 0.219543 0.109838 0.448461 -0.072536 0.021204 0.187597 -0.115392 0.00927 -0.089738

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.073041 0.31066 -0.033845 0.60237 0.162012 0.12014 0.010633 0.045166 0.076492 0.018999 0.081655 0.226766 0.035369 0.141642 0.095848 0.032434 0.234249Q29 -0.001953 0.257072 0.061305 0.647564 0.159356 0.130051 -0.033843 0.120761 0.052514 0.132297 0.055638 0.051271 -0.072135 0.099162 0.057194 0.036516 0.155159Q30 0.344859 0.164527 0.13826 0.362577 0.164553 0.088284 0.164434 0.165546 -0.033403 0.037559 0.429537 -0.022366 0.125457 -0.017442 0.010155 -0.020353 0.228241Tea

mwork

Superv

ision

Q31A 0.154163 0.765445 0.081238 0.063177 -0.015423 0.088729 0.052369 0.133703 0.041156 0.050533 0.033327 0.144078 -0.041443 0.197752 -0.005482 0.070623 -0.003386Q31B 0.13825 0.820485 0.025719 0.099846 0.055781 0.040813 0.100391 0.147449 0.087134 0.013122 -0.050975 0.094524 0.03401 0.100715 0.065489 0.121801 0.031082Q31C 0.159304 0.790832 0.019849 0.076469 0.16135 0.071979 0.015762 0.108904 0.034664 0.002037 0.016659 0.023075 0.089961 0.209597 0.038333 0.066499 0.00688Q31D 0.139018 0.796183 0.065174 0.160468 0.12338 0.073018 0.063494 0.113041 0.102014 0.022721 0.165849 0.118843 0.055472 0.031624 0.143515 0.019291 -0.062989Q31E 0.092477 0.755458 0.037367 0.313258 0.129648 0.084759 0.00505 0.069354 0.076049 0.123886 0.05438 0.114674 -0.067838 0.066414 0.122695 -0.052134 0.063328Q31F 0.117579 0.753162 0.108413 0.191741 0.153511 0.097271 0.141843 0.028436 0.043064 -0.001606 0.09785 0.065291 0.097899 0.016006 0.138703 0.09412 -0.007317Q31G 0.188533 0.799941 0.047689 0.213492 0.094436 0.124458 0.118685 0.073985 0.037297 0.059035 0.034549 0.096394 0.049982 0.131296 0.041372 0.039192 -0.014203

Lead

ership

Superv

ision

Q32A 0.022704 0.041483 0.052858 0.1512 0.02909 0.021964 0.67843 0.076839 0.076471 0.102331 -0.10368 0.210851 0.059952 0.072483 -0.058103 -0.1735 -0.218187Q32B 0.225032 0.076122 0.123218 0.149309 0.098879 0.117749 0.670635 0.082444 0.069864 0.11968 0.179255 0.087243 0.063395 0.011476 0.183179 0.101014 -0.076553Q32C 0.27348 0.097276 0.072142 0.158538 0.114214 0.100492 0.663569 0.101862 0.074058 0.067765 0.231698 0.026421 0.056974 0.119752 0.09512 0.013215 0.030015Q32D 0.253178 0.168463 0.031932 0.107448 0.18079 0.10407 0.632264 0.123772 0.139411 0.121281 0.080333 0.063425 0.155589 0.04995 0.098382 0.145543 0.099599Q33 0.347908 0.203234 0.115939 0.078624 0.204294 0.048901 0.426198 0.176711 0.006147 0.037677 0.196489 0.014074 0.144447 0.171915 0.16493 0.187093 0.297304Q34 0.279181 0.130683 0.119623 0.078686 0.319535 0.057408 0.48183 0.053456 0.155449 0.111741 0.076527 0.030629 0.160391 0.091997 0.130258 0.125914 0.291912Q35 0.423072 0.190416 0.182705 0.026758 0.18279 0.17256 0.406644 0.125858 0.07138 0.053231 0.152955 -0.060249 0.112639 0.077903 0.083091 0.210309 0.269667Q36 0.570918 0.175532 0.149841 0.111771 0.146448 0.173102 0.369285 0.164253 0.089567 0.150383 0.141375 -0.022348 0.153069 0.076346 0.07249 0.024752 0.222118

Lead

ership

Reward

s and

Recog

nition

Q37A 0.141173 0.094482 0.104919 0.066336 0.077011 0.838193 0.058844 0.035232 0.090049 -0.015915 0.012391 0.083941 0.068949 0.073075 0.056079 0.044929 -0.015637Q37B 0.153149 0.099605 0.106334 0.106523 0.093334 0.82726 0.073041 0.023443 0.076068 0.06096 0.090543 0.040173 0.102324 0.076578 0.026419 -0.006281 0.026816Q38 0.362748 0.109633 0.161971 0.095303 0.151453 0.536052 0.158823 0.159353 0.081265 -0.016077 0.119079 -0.109409 0.145324 0.108717 0.056471 0.048072 0.004697Q39 0.317991 0.304144 0.091465 0.071455 0.225938 0.460739 0.16377 0.159297 0.133495 0.018273 0.054806 0.079531 0.04968 0.208264 0.018052 0.317161 0.034066Q40 0.270294 0.154798 0.107834 0.049434 0.090485 0.786382 0.044246 0.06959 0.075272 0.036486 0.002641 0.094306 0.009008 0.117445 0.055485 0.043802 -0.031222Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.028744 0.011773 0.790407 0.114139 0.061577 0.100238 0.067771 0.071164 0.105066 0.039282 -0.138303 0.018797 0.057139 0.084274 0.06131 0.076375 -0.126139Q42 0.136485 0.200289 0.348664 0.117239 0.258402 0.184691 0.125428 0.14763 0.185139 0.085513 -0.013956 0.041496 0.108577 0.090164 -0.010959 0.266327 -0.288224Q43A 0.121887 0.041947 0.801374 0.070558 0.05375 0.077772 0.080636 0.089374 0.233714 0.094884 0.003558 0.007342 0.075681 0.060496 0.009929 -0.017469 0.040494Q43B 0.098711 0.075712 0.732444 0.03062 0.055189 0.032916 0.076747 0.048823 0.271029 0.071782 0.052072 0.046445 0.127396 0.079016 0.059297 -0.00221 0.019773Q43C 0.165388 0.10344 0.445843 -0.050303 0.046574 0.009642 0.001058 -0.079065 0.397231 -0.012318 0.349286 0.006294 0.237108 0.053349 0.066423 -0.015823 0.040164Q43D 0.158668 0.066105 0.399583 0.106507 0.118127 0.102053 0.087445 -0.008263 0.678567 0.009324 0.025391 0.009272 0.155117 0.097782 0.09411 -0.056577 0.060667Q43E 0.1061 0.059148 0.354511 0.037182 0.064429 0.061532 0.139655 0.020153 0.72374 0.076481 -0.032969 -0.009782 0.106805 0.106538 0.152898 0.067947 0.04088Q43F 0.118431 0.07847 0.293717 0.034761 0.079853 0.105756 0.064649 0.067948 0.728999 0.105522 0.000513 0.143049 0.07252 0.048423 -0.01296 0.136348 -0.044548Q43G 0.125657 0.1357 0.256435 0.024508 0.051847 0.084461 0.08159 0.087993 0.678423 0.122384 0.084084 0.121172 0.099133 -0.005865 0.019757 0.048205 -0.075633Q43H 0.196346 0.053925 0.116004 0.116594 0.077948 0.192383 0.031808 0.28452 0.354421 0.047047 0.089361 0.093692 -0.147173 -0.05837 0.020011 -0.112195 0.294451Q44 0.189145 0.112638 0.656519 -0.037037 0.075599 0.14414 0.085649 0.046461 0.297154 0.080246 0.175254 0.149063 0.061896 0.022988 -0.022128 0.125575 0.038933Q45 0.167203 0.063333 0.687437 -0.029036 0.092736 0.133594 -0.003036 0.062846 0.13772 0.062398 0.319984 0.108476 0.054829 0.008352 0.052823 -0.001526 0.081372

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.125694 0.064982 0.026209 0.095426 0.709747 0.089923 0.130861 0.139716 0.169105 0.08143 -0.07008 0.10112 0.19854 0.101953 0.101429 0.002326 0.020434Q46B 0.2657 0.100822 0.068851 0.07847 0.698801 0.153341 0.121289 0.202767 0.141532 0.03813 -0.013514 0.132194 0.13007 0.148985 0.047905 0.050547 0.068951Q46C 0.359643 0.118852 0.049856 0.013074 0.510715 0.186383 0.187932 0.267345 0.042012 0.032624 0.150257 0.081828 0.07197 0.147936 0.119488 0.078647 0.096464Q47A 0.213187 0.23074 0.183816 0.200678 0.59448 0.121868 0.068974 0.002232 0.037722 0.02674 0.258616 0.166105 0.047334 0.106219 0.058822 0.176202 -0.084579Q47B 0.155062 0.312277 0.13097 0.175205 0.630645 0.097055 0.077561 0.018633 -0.009271 0.09249 0.329894 0.100351 -0.017156 0.017853 0.078402 0.106044 -0.072907Q48 0.328338 0.223191 0.149839 0.11492 0.567788 0.05125 0.144381 0.21354 -0.004339 0.035379 -0.02719 0.153142 -0.072734 0.05644 0.165827 0.075377 0.008107

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.04145 0.227723 0.037467 0.172037 0.213199 0.023473 0.053631 -0.114214 0.154924 0.114507 0.139766 0.650662 0.028678 0.129817 0.023086 0.099713 -0.05816Q50 0.236853 0.161831 0.093858 0.199578 0.10442 0.104922 0.124141 0.111947 0.102476 0.064592 -0.026939 0.755371 0.09966 0.051325 0.068127 0.053764 0.008767Q51 0.257366 0.229854 0.160414 0.17603 0.182122 0.028307 0.114619 0.108799 0.034086 -0.00199 -0.072671 0.640825 0.085231 0.121818 0.028645 0.090225 0.042933Q52 0.555725 0.23635 0.06239 0.045193 0.161614 0.192173 0.118859 0.107287 0.046753 0.064535 0.052797 0.49578 0.068902 0.117908 0.077301 0.172893 0.007148Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 0.691896 0.174328 0.137491 0.136254 0.084762 0.255326 0.086046 0.256659 0.086732 0.115532 0.152313 0.112847 0.092037 0.098382 0.07842 0.029568 0.034346Q54 0.616156 0.112875 0.162976 0.158675 0.127253 0.097106 0.086419 0.162789 0.236753 0.1183 0.061059 0.030563 0.137867 0.104643 0.112798 0.129413 -0.162396Q55 0.660479 0.176483 0.061565 0.117579 0.184472 0.178769 0.105967 0.084606 0.149326 0.10637 0.028793 0.185589 0.113113 0.071732 0.099831 0.0623 -0.073883Q56 0.766343 0.171824 0.093248 0.111936 0.085198 0.097505 0.167129 0.146583 0.11896 0.142522 0.03449 0.087177 0.107245 0.082791 0.107661 0.014591 -0.05718Q57 0.725087 0.200734 0.10333 0.141287 0.188633 0.197643 0.125134 0.141022 0.1096 0.138824 0.150112 0.158923 0.120829 0.081356 0.050249 0.026038 0.029802Q58 0.690656 0.174752 0.103528 0.105184 0.240728 0.158445 0.14443 0.135388 0.149393 0.128362 0.129443 0.174099 0.077595 0.09865 0.093311 0.162609 0.013103Q59 0.604297 0.147733 0.255477 0.127459 0.178532 0.176308 0.16579 0.097242 -0.008061 0.146343 0.185702 0.105906 0.075604 0.141866 0.09674 -0.034848 0.079188Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. No Deletion: Mean Substitution.

Compa

nt Im

age a

nd

Commitm

ent

17

100

Table B4. Factorial Structure of the Spanish version of the survey. Pairwise deletion.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Q1 0.109725 0.102106 0.186379 0.05166 -0.055117 0.226418 0.093959 0.186354 0.092097 0.143815 -0.046818 0.563072 0.089488 0.201634 0.077189 -0.136401Q2 0.176288 0.204892 0.118458 0.250848 0.131926 0.161602 0.096371 0.2528 0.131799 0.192616 0.103223 0.506166 -0.029511 -0.131952 -0.046453 -0.202291Q3 0.133552 0.190978 0.0956 0.073727 0.225194 0.04942 0.087863 0.123122 0.254325 0.071832 0.190144 0.536337 0.167778 -0.036884 0.135778 -0.033368Q4 0.269905 0.103356 0.141132 0.093332 0.126805 -0.06223 0.155599 0.123164 0.1633 -0.031855 0.239592 0.592316 0.075648 0.061335 0.087652 0.091254Q5 0.178671 0.163818 0.079311 0.113689 0.121953 0.283073 0.060953 0.100738 0.083463 0.004996 0.148847 0.600326 0.155272 -0.037987 0.018202 0.27375

Work

Enviro

nmen

t

Employe

e Inv

olvem

ent

Q6A 0.197118 0.083504 0.09862 0.180188 0.142657 0.103092 0.307902 0.168708 0.543039 -0.031752 0.151403 0.293437 0.09081 -0.043506 -0.009777 -0.20065Q6B 0.258611 0.061253 0.027463 0.295218 0.051464 0.144216 0.186964 0.089853 0.604439 0.026164 0.133242 0.271448 0.084212 0.032684 -0.008691 -0.270218Q7 0.315434 0.091395 0.189908 0.204654 0.091705 0.042185 0.164139 0.225985 0.429168 0.240776 -0.05716 0.320549 0.204132 0.092808 -0.012183 0.069137Q8 0.261737 0.040872 0.144638 0.207736 0.158492 0.107947 0.124988 0.048398 0.630402 0.219712 -0.016683 0.123159 0.07208 0.084278 0.111385 0.205599Q9 0.176054 0.138654 0.117167 -0.02997 0.036427 0.092647 0.030221 0.225605 0.625233 0.138901 0.104711 0.023637 0.044738 0.060435 0.140718 -0.026477Q10 0.229808 0.172197 0.211382 0.151266 -0.101457 0.15126 0.181087 0.26899 0.488722 0.19183 0.04644 0.171083 0.051394 0.052665 -0.028301 0.21434Q11 0.095253 0.07925 -0.02512 0.05302 0.052651 0.088962 0.011766 0.624973 0.116574 0.032029 0.167769 0.099059 0.17416 0.358542 0.104422 -0.051973Emplo

yee I

nvolv

emen

t

Commun

icatio

ns

Q12A 0.08687 0.069275 0.235927 0.083158 0.193672 0.066156 0.228106 0.728243 0.150072 0.132449 0.123769 0.125083 0.074534 -0.004491 0.062815 0.033988Q12B 0.148481 0.087637 0.193354 0.065477 0.19222 -0.022457 0.218084 0.74561 0.159284 0.165837 0.109276 0.141838 0.076951 -0.027491 -0.056355 0.046754Q12C 0.179151 0.08394 0.134315 0.037839 0.223288 0.096958 0.208945 0.727916 0.155096 0.145534 0.159471 0.195486 -0.011402 0.02996 0.065896 -0.041796Q13 0.238245 0.147004 0.16837 0.221325 0.164144 0.162157 0.193451 0.135588 0.382929 -0.09896 0.087505 0.162951 0.403572 -0.00502 -0.070225 0.245713Q14A 0.01548 0.046391 0.117676 0.118277 0.249108 0.234323 0.639297 0.134327 0.17238 0.092318 0.023649 0.095934 0.062954 0.148267 0.125043 -0.202527Q14B 0.079741 0.342136 0.144759 0.023775 0.209234 0.098619 0.665369 -0.058488 0.151794 0.143338 0.028685 0.093457 -0.08085 0.055291 0.014547 0.037828Q14C 0.165587 0.173977 0.166213 0.037862 0.152762 0.012902 0.700409 0.214221 0.148305 0.023501 0.153575 0.109181 0.10323 0.116263 0.026052 0.085509Q14D 0.210274 0.160732 0.129274 0.142936 0.114203 0.018583 0.75455 0.184644 0.075389 0.021539 0.124033 0.064854 0.116474 -0.005373 0.023728 0.016375Q14E 0.133123 0.121508 0.054279 0.141484 0.272833 0.044712 0.648695 0.205819 0.016702 0.147199 0.15885 0.051753 0.08446 -0.011639 0.033041 0.046557

Commun

icatio

ns

Quality

Focus

Q15 0.12616 0.175331 0.106167 0.188721 0.3482 0.100645 0.135538 0.197618 0.208878 0.048796 0.527611 0.031937 0.13461 0.03809 -0.071259 0.128033Q16 0.127113 0.188906 0.207925 0.107224 0.244079 0.12821 0.274454 0.304839 0.077995 0.105735 0.442185 -0.009213 -0.131884 0.064789 -0.072011 0.114254Q17 0.210916 0.110225 0.149483 0.166963 0.202518 0.078074 0.178842 0.266395 0.183862 0.223645 0.517566 -0.007367 0.057518 0.165366 -0.059608 0.060994Q18 0.140022 0.069576 0.1255 0.0512 0.03842 0.003863 0.054213 0.073248 0.057351 0.054718 0.74273 0.203141 0.108549 0.12834 0.032094 -0.049518

Custom

er

Focus

Quality

Focus

Q19 0.118467 0.117602 0.264156 -0.052746 0.217073 -0.052315 0.17079 0.14887 0.106186 0.19773 0.223031 0.129901 0.253443 0.415265 -0.04708 -0.128205Q20 0.355193 0.133839 0.166956 0.060224 0.147873 0.002788 0.247093 0.14373 0.062924 0.367791 0.275257 0.088003 0.231577 0.125717 0.02065 -0.03905Q21 0.200046 0.182677 0.190222 0.197926 0.168733 0.20989 0.287358 0.118187 0.128176 0.362232 0.292448 0.032903 0.164993 -0.077583 0.145703 -0.130459Q22 0.158494 0.036274 0.10564 0.035719 -0.024397 0.140658 0.195911 0.191987 -0.04058 0.240098 0.64768 0.166531 0.235505 0.131747 0.174128 -0.049634Cus

tomer

Focus

Perform

ance

Manag

emen

tQ23 0.230199 0.137536 0.256756 0.22089 0.066336 0.139207 0.114957 0.222486 0.095467 0.522311 0.165703 -0.010765 0.068029 0.041019 0.012238 0.068787Q24 0.245736 0.177115 0.202882 0.144365 0.180673 0.264137 0.094799 0.136375 0.206919 0.628724 0.078412 0.053148 0.0845 0.041267 0.021536 0.076737Q25 0.364401 0.146518 0.096628 0.178688 0.120707 0.216507 0.172858 0.051044 0.119394 0.57023 0.144635 0.058352 0.106899 0.158588 0.043308 -0.053861Q26 0.286396 0.134083 0.161538 0.193312 0.172904 0.058107 -0.003018 0.271274 0.124827 0.561432 0.11921 0.147858 0.10297 0.128643 -0.05185 -0.041598Q27A 0.26032 0.055948 0.317481 0.172303 0.123739 0.224907 0.195952 0.097753 0.136569 0.361287 0.202506 0.202376 0.20285 -0.125159 0.242577 -0.110903Q27B 0.112183 0.136109 0.268962 0.143554 0.08198 0.08526 0.167239 0.162725 0.023259 0.328466 0.149721 0.094409 0.322419 -0.295985 0.025701 0.032907

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.404582 0.167271 0.10169 0.090369 0.141062 0.079916 0.109556 0.083572 0.156008 0.257464 0.132732 0.146169 0.507806 0.1785 -0.063801 0.014153Q29 0.24407 0.112323 0.035387 -0.01801 0.139627 0.09671 0.060001 0.118871 0.078609 0.134997 0.172587 0.121026 0.691347 0.018149 0.093801 -0.097456Q30 0.202619 0.191555 0.191976 0.227636 0.329781 0.13805 0.112776 0.041815 0.129896 0.113036 0.129976 0.144044 0.583764 0.051045 -0.014287 0.156001Tea

mwork

Superv

ision

Q31A 0.706893 0.129546 0.141436 0.099419 0.053506 0.274845 0.133233 0.036472 0.126053 0.147655 -0.006974 0.178822 0.074364 0.080368 0.018913 0.102021Q31B 0.774156 0.076137 0.134505 0.138629 0.099723 0.146475 0.058252 0.057146 0.158796 0.140783 0.028531 0.159496 0.049798 -0.033599 0.083664 0.020361Q31C 0.794221 0.130528 0.104036 0.113895 0.14356 0.11587 0.131517 0.126246 0.089624 0.135818 0.056905 0.060714 0.080803 -0.001792 0.085573 -0.010209Q31D 0.807921 0.087566 0.112272 0.125905 0.156804 0.104887 0.146226 0.137524 0.085045 0.071707 0.126126 0.001586 0.088851 0.04938 0.098151 0.048069Q31E 0.71244 0.081292 0.148522 0.075133 0.185286 0.098223 0.098604 0.069161 0.164339 0.106548 0.21107 0.122831 0.194262 0.121211 0.114762 -0.040609Q31F 0.763471 0.14801 0.236314 0.141166 0.192401 0.027268 0.054456 0.118062 0.067034 0.063472 0.154488 0.071523 0.087498 -0.006273 0.07468 -0.07266Q31G 0.732169 0.181084 0.165191 0.097784 0.245448 0.104133 0.025193 0.033375 0.194642 0.150902 0.079519 0.140378 0.060312 0.105116 -0.056685 -0.004305

Lead

ership

Superv

ision

Q32A 0.12736 0.126232 0.176286 0.11097 0.647021 0.06048 0.193174 0.180708 -0.037121 0.08375 0.037878 0.0916 0.121689 0.120005 0.187339 0.043603Q32B 0.168037 0.132539 0.175336 0.0617 0.754414 0.088634 0.226832 0.129069 -0.05215 0.062401 0.046634 0.089072 0.11338 0.083076 0.038152 -0.039814Q32C 0.175283 0.026518 0.202942 0.135271 0.618902 0.233009 0.158905 0.166531 0.032087 -0.032635 0.019477 0.062996 0.177844 0.119141 0.281533 -0.138465Q32D 0.214314 0.11767 0.14475 0.158137 0.721048 0.020169 0.144926 0.145171 -0.006744 0.166656 0.031838 0.109945 0.023864 0.129916 0.014121 0.065084Q33 0.285902 0.199848 0.164757 0.171595 0.598416 0.120683 0.157295 0.134978 0.269467 0.085516 0.142687 -0.017823 0.089595 -0.005619 -0.011356 -0.007173Q34 0.325346 0.186506 0.19553 0.125377 0.534026 0.146141 0.151045 0.19391 0.307854 0.04767 0.147408 -0.129399 0.121591 -0.114806 -0.036351 -0.040825Q35 0.172374 0.300349 0.243202 0.220135 0.514687 0.248969 0.087699 -0.032129 0.25973 0.17727 0.165848 0.149747 -0.020213 -0.018716 0.022921 -0.013844Q36 0.157373 0.218587 0.255472 0.237474 0.550941 0.19547 0.1532 -0.036976 0.151117 0.122321 0.162481 0.167345 0.044059 0.040154 0.137723 0.117638

Lead

ership

Reward

s and

Recog

nition

Q37A 0.088536 0.161424 0.150567 0.054242 0.055251 0.817273 0.083486 0.08444 0.101994 0.063556 0.022055 0.06858 0.121377 0.090848 0.029472 -0.108648Q37B 0.165603 0.106251 0.156281 0.171269 0.181447 0.708769 0.026577 0.035454 0.139825 0.073915 0.112143 0.113291 0.059963 0.063117 0.100808 -0.106023Q38 0.268409 0.283686 0.159734 0.180463 0.215483 0.647562 0.042858 0.013173 0.091526 0.21623 -0.01693 0.140424 0.013567 0.015008 -0.070565 0.114936Q39 0.242199 0.268798 0.103161 0.237621 0.223522 0.512195 0.052147 0.062499 0.085477 0.279315 0.119456 0.138175 0.024586 0.048537 0.035226 0.164166Q40 0.158754 0.258599 0.257173 0.084217 0.076548 0.697743 0.122692 0.056037 0.028485 0.083693 0.058842 0.034159 0.035339 -0.017024 0.066512 0.121085Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.147993 0.655907 0.180437 0.140736 -0.005458 0.293374 0.133591 0.018195 0.031307 -0.027073 0.034169 0.041619 0.176478 0.055751 -0.086459 -0.223227Q42 0.278724 0.45059 0.227069 0.128882 0.205417 0.172874 0.141418 -0.01046 0.12842 0.203219 -0.021956 0.070646 0.059139 0.112525 -0.008952 -0.116018Q43A 0.098446 0.804499 0.070843 0.047353 0.117201 0.146028 0.090646 0.043691 0.09299 0.032767 0.056231 0.107725 0.108525 0.017706 0.005424 -0.133876Q43B 0.056912 0.819159 0.128384 0.168672 0.066627 0.15223 0.069755 0.09112 0.071309 0.120011 0.110115 0.027028 0.077841 -0.071348 -0.013798 -0.085432Q43C 0.031332 0.804741 0.057011 0.160253 0.133884 0.031243 0.062634 0.07539 0.056139 0.108559 0.087669 0.021422 0.007454 0.032794 0.125468 0.071736Q43D 0.110991 0.749805 0.050958 0.142583 0.121806 0.000815 0.085078 0.057302 0.022038 0.108231 0.037718 0.086402 -0.000249 0.052294 0.34109 0.245691Q43E 0.085575 0.776439 0.070641 0.11012 0.156757 0.039722 0.122907 0.013889 0.039387 0.043191 0.0717 0.100011 0.053879 0.097986 0.313745 0.247477Q43F 0.186171 0.487038 0.094993 0.135841 0.174979 0.04068 0.096205 0.070103 0.103583 0.049491 0.128397 0.102582 0.027512 -0.025219 0.622843 0.031932Q43G 0.205977 0.371369 0.127546 0.129261 0.179353 0.090492 0.042417 0.048335 0.072488 0.007551 0.01169 0.103672 -0.027287 0.185223 0.678167 -0.104079Q43H 0.071667 0.283244 0.389848 0.095688 0.186651 0.103444 0.093239 0.047093 0.121071 -0.002387 -0.072327 0.043794 0.125115 -0.143407 0.423037 0.198125Q44 0.201744 0.561531 0.221519 0.164074 0.103664 0.253277 0.150852 0.116923 0.085806 0.012228 0.065647 0.052798 -0.000997 0.134387 0.19187 0.072838Q45 0.172702 0.603785 0.183243 0.231297 0.061888 0.178866 0.184074 0.081944 0.030069 0.09776 -0.022783 0.197427 0.07262 -0.064295 -0.127379 -0.039374

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.119108 0.205986 0.153133 0.797823 0.119851 0.120622 0.096136 0.078322 0.115958 0.08888 0.043888 -0.10387 0.063343 0.09546 0.048326 -0.082999Q46B 0.098249 0.202083 0.23339 0.81303 0.07885 0.038218 0.105344 0.039206 0.079103 0.107252 0.019001 -0.003008 0.094954 0.017601 0.031786 -0.033277Q46C 0.108927 0.15911 0.228655 0.750334 0.079692 0.103916 0.125784 0.080676 0.074244 0.132173 0.053569 0.091588 0.023564 0.047376 0.135439 0.06091Q47A 0.148285 0.152911 0.280946 0.680557 0.203628 0.122836 0.070555 0.070248 0.053999 0.123132 0.076686 0.2357 0.031511 0.074244 0.027111 0.028933Q47B 0.206088 0.212176 0.28402 0.661272 0.197213 0.062792 0.02527 0.035557 0.033431 0.070385 0.073327 0.217689 -0.004321 0.056165 0.046206 0.004956Q48 0.127507 0.156349 0.280015 0.614437 0.134582 0.227669 0.030816 0.007843 0.196252 0.053909 0.12767 0.109068 0.032485 0.122659 0.012243 0.089587

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.080487 0.046741 0.214105 0.188488 0.119801 0.081337 0.113637 0.220801 -0.035143 0.023061 0.169468 -0.033488 0.062315 0.633224 0.066834 0.062496Q50 0.133443 0.086515 0.432061 0.317709 0.163975 0.081196 0.119084 -0.002526 0.123358 0.08765 0.100924 0.146855 -0.003131 0.41515 0.030909 0.042257Q51 0.124207 0.051753 0.291463 0.252807 0.202448 0.190253 0.048312 -0.057669 0.190408 0.173421 0.229162 0.039411 -0.060787 0.521164 0.052535 -0.046524Q52 0.199656 0.283284 0.503014 0.15903 0.024547 0.293805 0.117375 0.202599 0.023025 0.105294 0.143425 0.074833 -0.003612 0.283225 -0.128458 0.221913Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 0.142655 0.177051 0.645688 0.29453 0.155575 0.127912 0.089561 0.174406 0.039039 0.224319 0.089162 0.070096 0.074037 0.045398 0.023764 0.033676Q54 0.219596 0.132806 0.613374 0.293137 0.216981 0.010898 0.101383 0.05077 0.082183 0.173559 0.127399 0.172741 0.083826 0.141638 0.059716 0.040674Q55 0.189213 0.152797 0.673915 0.213226 0.240458 0.134647 0.150752 0.090625 0.091344 0.054661 0.016836 -0.007775 0.071473 0.156476 0.053926 -0.079415Q56 0.11954 0.101731 0.789653 0.203022 0.169701 0.106496 0.112486 0.105791 0.080859 0.104295 0.031666 0.07698 0.08208 0.048732 0.067985 -0.026602Q57 0.112828 0.147745 0.767263 0.196727 0.192603 0.140055 0.144718 0.072042 0.058752 0.120156 0.12276 0.083252 0.053261 0.063151 0.045521 -0.001747Q58 0.269957 0.12557 0.563002 0.276158 0.119154 0.303698 0.025742 0.007373 0.185223 0.202534 0.12146 0.116127 0.018599 0.068493 0.016047 0.074993Q59 0.206521 0.08896 0.656633 0.212696 0.096001 0.220533 0.043383 0.208597 0.127399 -0.048494 0.154873 0.115061 0.025114 0.057628 0.125436 -0.069319Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Deletion: Pairwise.

Compa

nt Im

age a

nd

Commitm

ent

16

101

Table B5. Factorial Structure of the Spanish version of the survey. Listwise deletion.

1 2 3 4 5 6 7 8 9 10 11 12 13 14Q1 0.254267 0.507786 0.060489 0.440643 0.09472 0.23231 -0.000875 0.199111 0.057768 0.237764 0.216616 0.026412 -0.019239 -0.180304 -0.017011Q2 0.336496 0.536474 0.119051 0.359851 0.032597 0.201803 0.094448 0.319705 0.13098 0.179558 0.16862 0.077938 0.115668 0.230347 -0.028417Q3 0.389617 0.584383 0.094236 0.299044 0.096745 0.13519 0.015024 0.207573 -0.042576 0.021133 0.254399 0.223007 0.157905 0.23382 0.00649Q4 0.376955 0.52977 0.263773 0.329057 -0.022561 0.193503 0.119338 0.201654 -0.104877 0.09168 0.068362 0.197338 0.116867 0.157929 0.022684Q5 0.254179 0.274348 0.312333 0.615687 -0.01957 0.036403 0.073596 0.291318 -0.10635 0.242758 -0.028971 0.257747 0.091701 0.07281 -0.04626

Work

Enviro

nmen

t

Employe

e Inv

olvem

ent

Q6A 0.312733 0.547711 0.191688 0.330569 0.149087 0.031355 0.097229 0.224722 0.186201 -0.070961 0.227462 0.099717 -0.044944 0.104759 0.347022Q6B 0.442234 0.366738 0.21414 0.280758 0.05599 0.159021 -0.032746 0.271733 0.134569 0.108623 -0.00666 0.28749 0.052101 0.241509 0.308813Q7 0.364101 0.46467 0.126726 0.2049 0.152029 0.069067 0.351663 -0.121593 0.200245 -0.028544 -0.056545 0.35198 0.002249 -0.126613 -0.21872Q8 0.467183 0.327824 0.202803 0.188952 0.177563 0.263932 0.350337 -0.037177 0.209681 0.10076 -0.088786 0.168521 0.051544 0.031481 0.08734Q9 0.277824 0.335876 -0.082326 0.160432 0.130128 0.26795 0.070593 -0.013339 0.073568 0.135132 0.070922 0.044579 -0.049844 0.580628 0.083352Q10 0.261331 0.242503 -0.016808 0.382653 0.085008 0.113035 0.729756 -0.032736 0.025678 0.030261 0.090092 -0.003583 0.049589 0.068868 -0.037046Q11 0.273373 0.736364 0.079314 0.221861 -0.054289 0.098956 0.18449 0.196213 -0.148036 0.125668 -0.087781 -0.048395 0.082088 -0.054046 0.232522Emplo

yee I

nvolv

emen

t

Commun

icatio

ns

Q12A 0.179891 0.736834 0.297589 0.081428 0.265151 0.037576 0.117255 0.041935 0.062982 -0.009619 0.016544 -0.059101 0.003146 0.188223 0.101083Q12B 0.269678 0.81552 0.160301 0.133487 0.121568 0.032676 0.076108 0.001683 0.226163 0.014527 0.052141 -0.068153 -0.090554 0.052914 0.028844Q12C 0.217277 0.838393 0.124604 0.128814 0.218067 0.151914 0.059904 0.031065 0.066413 0.069728 -0.004684 0.045178 -0.102425 -0.059484 0.026702Q13 0.433175 0.374278 0.116807 0.560729 0.112458 0.065658 0.145468 0.168512 -0.11848 0.015743 0.006523 0.181056 -0.132438 0.182536 -0.056533Q14A 0.189066 0.233817 0.18261 0.321436 0.321806 0.085669 0.042751 0.278788 0.667954 -0.014268 -0.046074 0.081913 0.026264 0.101894 -0.002954Q14B -0.020645 -0.039008 0.192663 0.605406 0.138208 0.198451 0.26529 0.064856 0.44989 0.114355 -0.235113 -0.050573 0.061608 -0.072672 0.125341Q14C 0.460711 0.276067 0.105201 0.217458 0.175062 0.153383 0.379847 0.042953 0.422129 0.258899 0.080134 0.017899 -0.052804 0.022482 0.094319Q14D 0.179739 0.26601 0.223724 0.40804 0.064068 0.038408 0.62486 0.223 0.0868 -0.008609 0.211585 0.102861 -0.043811 0.037112 0.13004Q14E 0.190112 0.460131 0.284642 0.089438 0.275613 0.183736 0.292043 0.132223 0.172156 0.124793 0.247256 0.084312 0.158304 0.363007 -0.120628

Commun

icatio

ns

Quality

Focus

Q15 0.122902 0.43188 0.331318 0.245119 0.162855 0.609571 0.125315 0.022594 0.073685 0.005479 0.189841 -0.024479 0.030811 -0.025008 -0.077466Q16 0.238899 0.266442 0.275789 0.16255 0.268973 0.648053 0.122907 0.132311 -0.004245 -0.072542 0.090443 -0.01653 0.097945 0.155296 0.042045Q17 0.311185 0.253313 0.232051 0.152883 0.062817 0.723564 0.057463 0.025839 0.147096 0.209498 -0.043662 0.034643 0.147652 0.092523 -0.006958Q18 0.294094 0.474904 0.11402 0.061544 0.097699 0.384603 0.129869 -0.020109 0.058387 0.249312 0.047331 0.382695 0.050135 0.01931 0.121235

Custom

er

Focus

Quality

Focus

Q19 0.191777 0.515397 0.203973 0.096051 0.191016 0.342165 0.006395 -0.027005 0.015044 0.13592 0.11206 0.011738 0.088166 0.091576 0.460212Q20 0.453172 0.283402 0.225472 0.156571 0.191298 0.25559 0.022149 0.170102 0.057088 0.561245 -0.113259 0.120679 0.034649 -0.019534 0.149419Q21 0.245303 0.202394 0.363465 0.239041 0.25199 0.512403 0.163525 0.300883 0.148758 0.113909 0.192908 0.081707 -0.015866 0.06163 0.143539Q22 0.200398 0.502965 0.055453 0.064198 0.304132 0.369889 0.190441 0.269792 -0.124159 0.115053 0.107361 0.17243 0.133057 -0.248369 0.055506Cus

tomer

Focus

Perform

ance

Manag

emen

tQ23 0.289024 0.403049 0.114018 0.265082 0.326903 0.29896 -0.047358 -0.086389 0.378739 0.157131 0.110809 0.202838 -0.029359 0.068068 -0.236116Q24 0.289047 0.524342 0.102598 0.085403 0.145049 0.190196 0.185748 0.150203 0.300302 0.031033 0.135822 0.06392 0.178356 0.305889 -0.306169Q25 0.478525 0.414029 0.078554 0.216818 0.135603 0.338838 0.201788 0.061281 0.389189 0.082696 0.225225 0.053825 0.027436 0.249679 -0.048541Q26 0.402824 0.455427 0.219893 0.058805 0.147528 0.279122 0.231464 0.151456 0.121577 -0.013095 0.05787 0.390409 -0.130188 0.145912 -0.011071Q27A 0.278895 0.126118 0.421264 0.227079 0.426704 0.321594 0.082154 0.162746 0.201744 0.016186 0.158108 0.310202 0.099983 0.141835 0.003991Q27B 0.201311 0.176477 0.358875 0.108733 0.067957 0.149124 0.218321 0.105053 0.004987 0.114757 0.667931 0.009843 -0.034718 0.067 0.026561

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.552828 0.522612 0.171869 0.195151 0.299619 -0.06568 0.115664 0.024288 0.046755 -0.123755 0.094245 0.232199 0.14722 0.194766 0.103582Q29 0.449255 0.446722 0.206543 0.149679 0.220607 -0.085893 0.109938 0.132612 0.067708 0.111492 0.081505 0.436262 0.108879 0.075656 0.044654Q30 0.251859 0.325453 0.452219 0.287371 0.31725 0.087572 0.041083 0.15669 -0.054491 0.023074 0.38126 0.109624 0.106516 0.130808 0.10797Tea

mwork

Superv

ision

Q31A 0.670948 0.10926 -0.030751 0.50371 0.090166 0.128914 0.101762 0.150706 0.065686 0.111386 -0.039764 0.068624 0.038866 -0.147872 -0.224407Q31B 0.806201 0.275774 0.128701 0.127358 0.149768 0.150127 0.058146 0.101386 0.065869 -0.010774 0.112358 0.118582 0.016599 0.011406 -0.022355Q31C 0.767503 0.31047 0.170852 0.183275 0.130572 0.163242 0.123797 0.030529 -0.05635 0.005486 0.109468 0.195021 -0.061297 -0.08015 0.015985Q31D 0.83977 0.231186 0.230711 0.166637 -0.034295 0.183979 0.131781 0.069996 1.47E-05 0.098161 0.023792 -0.123255 0.030164 -0.025689 0.006004Q31E 0.78595 0.266355 0.160975 0.240786 0.037582 0.050733 0.101735 -0.05378 0.066905 0.140007 -0.035674 -0.205081 0.216578 0.107782 -0.001872Q31F 0.7878 0.257888 0.25394 0.185745 0.035124 0.154892 0.004112 0.0939 0.028299 0.043507 0.112863 0.08782 -0.040492 0.091892 0.00924Q31G 0.809916 0.14801 0.260073 0.177359 0.145855 0.077609 0.167175 0.065542 -0.014535 0.099531 -0.091483 0.133092 0.047034 0.062631 0.019956

Lead

ership

Superv

ision

Q32A 0.306706 0.183494 0.361083 0.10433 0.172918 0.128652 0.125215 0.009714 0.025778 0.609894 0.253666 0.056428 0.047771 0.199333 -0.066201Q32B 0.383948 0.211811 0.386027 0.121315 0.316238 0.219891 0.082768 0.252328 0.324566 0.058916 0.186111 0.171422 0.262555 0.103444 -0.08712Q32C 0.46952 0.126719 0.335755 0.24141 0.301351 -0.015661 -0.155575 0.3832 0.086115 0.207745 0.050092 0.250324 0.253441 0.029347 0.010354Q32D 0.42987 0.423527 0.460836 0.127289 0.21432 0.004771 -0.068953 0.038824 0.182979 -0.052149 -0.123581 0.261092 0.008352 0.198935 -0.020264Q33 0.609973 -0.101094 0.442413 0.142472 0.146032 0.107002 0.078073 0.021577 0.162038 0.186935 0.000125 0.141 0.206823 0.23787 0.185283Q34 0.757374 0.231875 0.250121 0.161429 0.120631 0.122021 -0.033113 0.124151 0.114602 0.020059 0.222694 -0.069992 0.081941 0.094954 0.070793Q35 0.321515 0.186387 0.677989 0.264886 0.17354 0.170078 0.043583 0.075923 0.265555 -0.114165 0.254772 -0.004463 0.062455 0.055915 0.043986Q36 0.300736 0.157138 0.70242 0.29532 0.08234 0.141014 0.123863 -0.023054 0.237773 0.13702 0.098105 0.053468 0.115289 -0.045654 0.141748

Lead

ership

Reward

s and

Recog

nition

Q37A 0.135919 0.17588 0.172028 0.420516 0.065661 0.137694 0.287987 0.696619 0.141784 0.001589 0.049678 -0.022359 -0.067533 0.077187 -0.008236Q37B 0.250871 0.281635 0.366847 0.179055 0.240315 0.08721 -0.123391 0.631088 0.086412 0.084847 0.114581 0.055639 -0.007947 -0.092846 0.050066Q38 0.121391 0.052603 0.446027 0.467814 0.207078 0.307215 0.293607 0.371248 0.159017 0.085848 -0.143719 0.001167 0.024338 0.022208 -0.123579Q39 0.318129 0.092069 0.350383 0.429686 0.119109 0.323861 0.310685 0.264253 -0.087191 0.199895 -0.265898 0.048426 -0.111219 0.131274 -0.084463Q40 0.006136 -0.065182 0.229096 0.500468 0.283896 0.178884 0.430665 0.245806 0.132285 0.218503 0.076019 0.014308 0.091407 0.080197 -0.112795Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.270123 0.078023 0.292408 0.739564 0.093777 -0.090532 0.062211 0.125448 0.215774 -0.005176 0.063672 0.021821 0.176495 0.044808 0.031766Q42 0.544006 0.223833 0.2343 0.336005 -0.083681 -0.01412 0.153396 0.023667 0.248952 0.408311 0.135863 -0.057197 0.033126 0.116685 0.073697Q43A 0.335369 0.178868 0.138406 0.805293 -0.014048 0.066334 -0.031607 -0.022744 0.175288 0.044293 0.05396 0.008644 -0.060872 0.075615 0.101725Q43B 0.134945 0.226833 0.312774 0.678725 0.289879 0.121661 0.143844 0.005235 0.013909 -0.124506 0.239723 0.087806 0.040009 0.154699 0.107849Q43C 0.080002 0.015403 0.352382 0.512569 0.110317 0.306345 0.108647 -0.005235 -0.241425 -0.062666 -0.159183 0.01127 0.408653 0.083505 0.23901Q43D 0.275172 0.106578 0.346972 0.649574 -0.015016 0.258018 0.279115 -0.075678 -0.125939 0.064692 0.113995 0.083106 0.236831 0.023433 0.242238Q43E 0.182901 0.206546 0.283365 0.641939 0.117588 0.27355 0.332627 -0.027661 -0.137929 0.167414 0.062312 0.005439 0.278995 -0.055376 0.117448Q43F 0.352716 0.180689 0.58237 0.198768 0.025345 0.100133 0.327997 0.095241 -0.036046 0.245976 0.143259 0.12615 0.23624 -0.10639 0.170719Q43G 0.429301 0.141734 0.558525 0.329077 0.045818 0.075982 0.09167 0.157788 0.072335 0.17419 0.009371 0.214141 0.254036 -0.111437 0.223563Q43H 0.328568 0.07331 0.287583 0.2512 0.250293 0.214981 0.00825 -0.034637 0.095032 0.070263 -0.01117 0.016677 0.654706 -0.017428 -0.03804Q44 0.394857 0.119317 0.239736 0.684557 0.173068 0.120647 0.110101 0.147536 0.116977 0.120195 -0.101611 0.063901 0.059811 -0.039827 -0.070301Q45 0.196922 0.283115 0.147214 0.701912 0.116102 0.039878 0.026057 0.138248 0.206005 -0.017489 0.185485 -0.057303 -0.066081 -0.005348 -0.152924

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.349535 0.021313 0.672033 0.285668 0.277423 0.271153 -0.022804 0.154707 0.033416 -0.063154 0.130516 0.005751 -0.086392 -0.019638 0.146785Q46B 0.33806 0.121788 0.588412 0.221305 0.297195 0.335744 0.076325 0.103751 -0.042208 -0.066826 0.237532 0.141896 -0.053362 0.104066 0.10742Q46C 0.297017 0.164405 0.552209 0.24396 0.34363 0.324824 0.155118 0.096136 0.064531 0.158438 0.154552 0.193677 0.036374 -0.052908 -0.003891Q47A 0.167948 0.174468 0.707633 0.252099 0.196326 0.204095 0.037329 0.143185 0.065995 0.30269 -0.095199 0.01093 0.06203 0.046522 -0.079805Q47B 0.18258 0.299663 0.759209 0.173373 0.16902 0.048719 0.019535 0.064423 -0.044976 -0.041906 0.058623 -0.030446 0.098339 0.026042 -0.131286Q48 0.128526 0.240076 0.734101 0.246941 0.412402 0.022598 -0.012203 0.086627 0.00688 0.20618 -0.00708 0.006755 0.055526 0.064496 -0.017785

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.074355 0.728637 0.215417 0.039974 0.13294 0.203233 -0.024902 -0.004417 -0.038194 0.098632 -0.001469 0.019368 0.169373 -0.019274 -0.320623Q50 0.208542 0.233202 0.558141 0.210969 0.350029 0.194942 0.052979 0.129456 -0.020359 0.394458 -0.160926 -0.113161 0.043827 -0.044477 -0.03076Q51 0.470477 0.14563 0.299098 0.093185 0.156674 0.44387 -0.065437 0.158154 0.044156 0.252967 -0.324566 0.002692 0.055945 0.235679 0.050949Q52 0.053802 0.345693 0.021198 0.499736 0.399897 0.138663 0.317344 0.14046 -0.271476 0.084843 -0.132067 0.015451 -0.025814 0.167864 -0.122024Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 -0.051893 0.300495 0.370747 0.137345 0.708959 0.218159 0.04521 -0.167389 0.046629 -0.022317 0.058885 0.066524 0.126146 -0.021328 -0.064091Q54 0.236727 0.467399 0.491915 0.091028 0.402795 0.08622 0.06735 0.007646 0.094303 0.126351 -0.069803 0.216416 -0.214848 0.052607 -0.00684Q55 0.171389 -0.042639 0.18597 0.163224 0.811596 0.031775 0.023172 0.114571 0.187897 0.14903 -0.145303 -0.01301 0.047469 0.066767 0.034323Q56 0.166925 0.172374 0.111516 0.05558 0.899727 0.12658 0.030873 0.021844 0.051848 0.004712 -0.043085 0.021793 0.027405 0.104141 0.022861Q57 0.034529 0.267217 0.186089 0.083391 0.816836 0.056772 -0.009535 0.086988 0.004771 0.058145 0.166311 0.079666 0.014522 0.012309 0.039845Q58 0.098817 0.141526 0.518362 0.041532 0.6031 0.144034 0.343636 0.056467 0.0933 -0.029444 0.145606 0.163865 -0.110355 -0.113757 -0.069362Q59 0.062532 0.189957 0.452708 0.008782 0.61498 0.029027 0.259041 0.258264 0.004033 0.024998 0.13061 -0.224133 0.207544 -0.080153 0.046396Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Deletion: Listwise.

Compa

nt Im

age a

nd

Commitm

ent

15

102

Table B6. Factorial Structure of the Spanish version of the survey. Mean substitution.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Q1 0.100322 0.103161 0.205975 0.037479 -0.019049 0.200939 0.078491 0.111902 0.134725 0.574837 0.126599 -0.038764 0.104382 0.148469 0.036903 -0.211524 0.04553Q2 0.169105 0.182952 0.12943 0.210799 0.124347 0.14399 0.101693 0.1946 0.210867 0.524589 0.132261 0.091366 -0.014543 -0.127889 -0.060198 -0.099294 0.094762Q3 0.138272 0.150578 0.078943 0.077032 0.170233 0.044557 0.087625 0.100588 0.128643 0.563154 0.168659 0.15468 0.133838 0.031124 0.161137 0.15191 0.121141Q4 0.2603 0.069058 0.102141 0.082381 0.098149 -0.052796 0.146096 0.007337 0.133995 0.60392 0.115904 0.221075 0.054817 0.091047 0.097373 0.11821 -0.043752Q5 0.169814 0.138903 0.075348 0.095555 0.106294 0.272372 0.056646 0.026812 0.10144 0.604901 0.058652 0.14773 0.13583 -0.020996 0.031406 0.173436 -0.200232

Work

Enviro

nmen

t

Employe

e Inv

olvem

ent

Q6A 0.187162 0.046614 0.099481 0.149388 0.10402 0.095846 0.294576 -0.001247 0.147405 0.336331 0.489918 0.14593 0.045837 0.002963 -0.008739 0.105975 0.260345Q6B 0.238978 0.056599 0.034526 0.245491 0.039992 0.124829 0.165889 0.022561 0.047123 0.29219 0.578029 0.127469 0.054465 0.034928 -0.021987 0.003888 0.292233Q7 0.300275 0.084397 0.204858 0.186549 0.118796 0.045763 0.157498 0.16895 0.194668 0.31151 0.463933 -0.035692 0.209865 0.034383 -0.052891 -0.038543 -0.133397Q8 0.235376 0.022203 0.102615 0.182853 0.139056 0.119124 0.122093 0.220552 0.055453 0.14123 0.590145 -0.029201 0.040793 0.122157 0.116401 0.24646 -0.100842Q9 0.140553 0.110394 0.116626 -0.015315 0.054129 0.091944 0.036738 0.106832 0.167014 0.00207 0.647162 0.109643 0.095546 0.021057 0.127714 -0.02953 0.014367Q10 0.20406 0.159911 0.222048 0.13351 -0.053759 0.14014 0.19285 0.134578 0.210942 0.15394 0.547649 0.085593 0.077043 -0.017648 -0.056715 -0.012511 -0.215491Q11 0.086365 0.081415 0.002919 0.042999 0.099559 0.082468 0.015093 -0.023524 0.551197 0.087473 0.203551 0.201799 0.227657 0.259538 0.053969 -0.241549 -0.027528Emplo

yee I

nvolv

emen

t

Commun

icatio

ns

Q12A 0.082566 0.045293 0.184357 0.087211 0.145347 0.065429 0.207369 0.15262 0.756409 0.121087 0.107273 0.102898 0.077298 0.032833 0.069808 0.076842 -0.022788Q12B 0.133884 0.065019 0.151135 0.060744 0.132928 -0.019834 0.170495 0.172893 0.78353 0.153147 0.111384 0.085948 0.042456 0.021254 -0.042665 0.155585 0.008864Q12C 0.158645 0.060788 0.105385 0.036881 0.164726 0.094123 0.163775 0.146233 0.751322 0.183144 0.121191 0.138974 -0.012063 0.054535 0.076057 0.015769 0.047067Q13 0.210418 0.106308 0.110226 0.207473 0.063236 0.154475 0.176079 -0.026383 0.191162 0.203212 0.229482 0.031413 0.359079 0.117933 -0.017287 0.502233 -0.041441Q14A 0.017503 0.04031 0.094707 0.111962 0.24399 0.22163 0.604701 0.108293 0.144447 0.132518 0.149191 0.01599 0.053573 0.161545 0.113441 -0.055989 0.229349Q14B 0.078401 0.274058 0.117747 0.020415 0.166638 0.106901 0.658161 0.178644 -0.030602 0.100109 0.11508 0.016024 -0.078975 0.086212 0.033932 0.119099 0.011793Q14C 0.161672 0.146855 0.148506 0.033822 0.120564 0.017762 0.704315 0.03395 0.189051 0.106548 0.127715 0.144912 0.096167 0.121496 0.031493 0.090219 -0.047252Q14D 0.192625 0.129483 0.125152 0.129147 0.105577 0.022272 0.757374 0.029473 0.165102 0.067707 0.06712 0.129955 0.102917 -0.014218 0.026191 0.005138 -0.003368Q14E 0.108761 0.104253 0.072686 0.129612 0.253749 0.046723 0.641909 0.125664 0.142394 0.031774 0.049095 0.157275 0.099425 -0.039285 0.009772 -0.010402 -0.082356

Commun

icatio

ns

Quality

Focus

Q15 0.129644 0.150506 0.106324 0.1843 0.29006 0.088133 0.142978 0.083325 0.177635 0.070359 0.154123 0.528539 0.120877 0.079406 -0.044385 0.206594 -0.013874Q16 0.112109 0.170853 0.207248 0.086226 0.228919 0.127408 0.277834 0.128631 0.237787 0.014929 0.088027 0.465626 -0.136309 0.073684 -0.062842 0.040698 -0.087522Q17 0.194208 0.102079 0.140606 0.149386 0.165192 0.068086 0.158015 0.234396 0.257863 0.017949 0.163156 0.521707 0.039753 0.1881 -0.060376 0.088864 0.010135Q18 0.135475 0.068783 0.122129 0.046596 0.036039 0.013242 0.064559 0.04466 0.044446 0.196431 0.059695 0.722067 0.111005 0.121719 0.014932 -0.025668 0.003985

Custom

er

Focus

Quality

Focus

Q19 0.115063 0.103304 0.22013 -0.063359 0.213708 -0.069529 0.167299 0.217922 0.112585 0.113109 0.101658 0.202626 0.282448 0.373987 -0.038346 -0.090928 0.131995Q20 0.348974 0.112068 0.134843 0.045539 0.108572 -0.009265 0.225401 0.406323 0.149168 0.11545 0.011884 0.231112 0.220673 0.168636 0.026825 0.075416 0.108583Q21 0.194398 0.142835 0.173394 0.171915 0.157831 0.187826 0.263816 0.386429 0.095183 0.060313 0.10452 0.269124 0.174595 -0.061861 0.155032 -0.020681 0.211448Q22 0.145647 0.019765 0.108712 0.02338 -0.008919 0.122584 0.170915 0.239669 0.163624 0.160191 -0.020346 0.616527 0.280204 0.083476 0.159093 -0.13312 0.031272Cus

tomer

Focus

Perform

ance

Manag

emen

tQ23 0.204359 0.116954 0.21318 0.192839 0.048216 0.130129 0.09964 0.57637 0.203741 0.025902 0.090788 0.144711 0.031568 0.086124 0.007205 0.054543 -0.036754Q24 0.222135 0.141722 0.166779 0.13109 0.147004 0.259064 0.097434 0.649076 0.121832 0.064931 0.191526 0.055832 0.07226 0.092914 0.03818 0.083944 -0.067821Q25 0.331455 0.112607 0.087105 0.160022 0.096873 0.198326 0.154182 0.581479 0.047933 0.075921 0.1211 0.119008 0.113169 0.180792 0.047713 -0.037526 0.038209Q26 0.273437 0.117831 0.168256 0.190253 0.163087 0.059522 0.006298 0.519964 0.231812 0.148427 0.169207 0.104847 0.125477 0.118278 -0.078882 -0.132175 -0.070959Q27A 0.246506 0.027095 0.289963 0.162433 0.125278 0.203126 0.180638 0.369862 0.096507 0.197166 0.128632 0.180024 0.239687 -0.1144 0.234136 -0.028196 0.103049Q27B 0.094155 0.102673 0.208297 0.124846 0.034309 0.060342 0.140712 0.372096 0.178093 0.116691 -0.06335 0.116577 0.269279 -0.212457 0.05291 0.217025 0.055403

Perform

ance

Manag

emen

t

Teamw

orkQ28 0.375058 0.12977 0.114612 0.088199 0.14503 0.085316 0.107251 0.249152 0.055123 0.128933 0.187597 0.131849 0.532396 0.122536 -0.072684 -0.00578 -0.064443Q29 0.227435 0.102039 0.050823 -0.006716 0.154399 0.095755 0.055527 0.116817 0.094561 0.10965 0.110003 0.168765 0.692957 -0.02872 0.046551 -0.00155 0.057155Q30 0.182503 0.154603 0.164656 0.215402 0.286123 0.131757 0.116588 0.153399 0.048721 0.138523 0.089628 0.096824 0.567385 0.086184 0.013447 0.274187 -0.07035Tea

mwork

Superv

ision

Q31A 0.693614 0.111169 0.131149 0.087777 0.045517 0.259953 0.114293 0.164322 0.045512 0.181862 0.108125 -0.004605 0.072127 0.085403 0.015631 0.061016 -0.056801Q31B 0.764352 0.064451 0.146449 0.13359 0.101189 0.139916 0.056902 0.11854 0.044485 0.139692 0.170327 0.035993 0.067161 -0.06359 0.064603 -0.008662 -0.0612Q31C 0.786132 0.104327 0.103275 0.103933 0.132331 0.112155 0.127355 0.142664 0.108112 0.070755 0.095779 0.055865 0.073071 -0.002094 0.077594 0.000729 0.008872Q31D 0.809032 0.073569 0.105664 0.114668 0.132493 0.09582 0.133381 0.090089 0.118627 0.020896 0.069576 0.11792 0.071131 0.068368 0.09546 0.061822 -0.016496Q31E 0.706636 0.069479 0.141697 0.07447 0.17931 0.087468 0.091749 0.095674 0.064731 0.124486 0.138621 0.2011 0.185939 0.115669 0.101381 -0.013805 0.055028Q31F 0.759554 0.128848 0.223308 0.133094 0.158671 0.019808 0.053822 0.08857 0.124006 0.084284 0.032923 0.137642 0.085376 0.032496 0.086253 0.060939 0.101885Q31G 0.723779 0.157908 0.14055 0.088137 0.214503 0.109929 0.04409 0.153846 0.023472 0.142822 0.169706 0.061122 0.058561 0.131475 -0.054155 0.066374 0.005409

Lead

ership

Superv

ision

Q32A 0.117304 0.109077 0.162384 0.105974 0.656319 0.067359 0.186003 0.037077 0.148577 0.067612 0.000782 0.054035 0.13259 0.066924 0.152838 -0.085786 -0.092958Q32B 0.156801 0.108024 0.15723 0.066952 0.7432 0.089797 0.209939 0.035193 0.107263 0.083185 -0.048517 0.05036 0.105353 0.067319 0.017893 -0.001076 0.007476Q32C 0.162017 0.017 0.18256 0.134028 0.622674 0.212186 0.146517 -0.045509 0.142997 0.056035 0.031101 0.02437 0.2019 0.088178 0.240687 -0.084969 0.106989Q32D 0.188367 0.116993 0.146827 0.141686 0.71074 0.017651 0.136675 0.151216 0.089936 0.098992 0.039534 0.061351 0.024274 0.088966 -0.008825 -0.042452 -0.12674Q33 0.249996 0.168255 0.152593 0.151278 0.570625 0.105674 0.116151 0.127065 0.134134 0.005092 0.211757 0.139138 0.061612 0.043627 -0.009278 0.250351 0.117875Q34 0.287561 0.167255 0.173024 0.112884 0.480161 0.123808 0.111697 0.105523 0.186327 -0.088369 0.219265 0.128448 0.084415 -0.040302 -0.029332 0.324711 0.182977Q35 0.150726 0.2434 0.206524 0.192095 0.475462 0.234146 0.052587 0.246014 -0.021398 0.167145 0.175029 0.124792 -0.058691 0.065116 0.065238 0.268858 0.154253Q36 0.156335 0.196162 0.221566 0.2075 0.510315 0.177949 0.12017 0.161758 -0.013208 0.180825 0.083947 0.134739 0.006016 0.102891 0.148682 0.256647 0.028591

Lead

ership

Reward

s and

Recog

nition

Q37A 0.083783 0.133142 0.139993 0.05044 0.045754 0.806716 0.076572 0.068514 0.087185 0.077215 0.085547 0.014929 0.122576 0.093468 0.029188 -0.018299 0.09995Q37B 0.153129 0.073786 0.147488 0.154047 0.157524 0.693304 0.029916 0.066789 0.039031 0.110615 0.123656 0.107125 0.056922 0.068083 0.105399 -0.018701 0.109066Q38 0.238815 0.248568 0.17369 0.17156 0.208709 0.637712 0.041445 0.215589 0.004979 0.140837 0.096387 -0.013454 0.005856 0.002117 -0.075189 0.07431 -0.108696Q39 0.216963 0.234585 0.116127 0.221306 0.223341 0.51575 0.05286 0.243713 0.022915 0.108632 0.130251 0.126341 0.043092 0.030396 0.011214 0.028425 -0.164609Q40 0.144511 0.226694 0.229473 0.073693 0.057018 0.694445 0.128327 0.108748 0.042589 0.041725 0.01981 0.053157 0.020059 0.020079 0.068906 0.107693 -0.061419Rew

ards a

nd

Recog

nition

Benefi

ts

Q41 0.137674 0.633559 0.154759 0.129288 -0.014962 0.279051 0.10517 0.003911 0.032861 0.061469 -0.006411 0.014437 0.169926 0.084354 -0.087151 0.01068 0.260721Q42 0.253358 0.42381 0.205243 0.11642 0.18104 0.164955 0.110482 0.217642 -0.000793 0.096411 0.088448 -0.057539 0.017602 0.169815 -0.025721 0.097231 0.177751Q43A 0.089031 0.781068 0.061367 0.042682 0.090544 0.135712 0.083998 0.042231 0.034518 0.120366 0.047658 0.037123 0.09054 0.040172 0.015124 0.022692 0.133094Q43B 0.042635 0.788424 0.126278 0.139437 0.050032 0.125807 0.069774 0.103378 0.052728 0.034016 0.067392 0.09663 0.063271 -0.066903 -0.011724 -0.007399 0.057639Q43C 0.017952 0.76514 0.063642 0.127533 0.140556 0.022883 0.068464 0.067456 0.026587 -0.00926 0.098906 0.110631 0.015902 -0.030858 0.124144 -0.058261 -0.147136Q43D 0.096205 0.730172 0.047175 0.129002 0.11897 -0.001359 0.077054 0.068198 0.03774 0.05376 0.053617 0.058224 0.006602 0.010872 0.329846 0.00944 -0.260374Q43E 0.069965 0.734999 0.05353 0.091989 0.140208 0.029243 0.107297 0.028647 0.006327 0.065275 0.054867 0.077045 0.047128 0.061691 0.342314 0.049911 -0.23023Q43F 0.162454 0.466267 0.078252 0.122117 0.167899 0.037978 0.079378 0.055224 0.061427 0.095126 0.0941 0.106802 0.018098 -0.012712 0.601865 0.054225 -0.042522Q43G 0.193322 0.346659 0.09915 0.122407 0.169668 0.103528 0.042874 0.003982 0.036401 0.085761 0.091315 -0.00795 -0.010599 0.183195 0.646925 -0.100097 0.064575Q43H 0.074794 0.230506 0.324085 0.07865 0.099026 0.083505 0.091006 0.07688 0.077293 0.099682 -0.001522 -0.095687 0.035285 -0.010889 0.453445 0.371042 -0.008478Q44 0.172423 0.496346 0.160827 0.135795 0.064628 0.256956 0.134381 0.049388 0.140779 0.071852 0.020413 0.030341 -0.016104 0.22799 0.168395 0.180738 0.048001Q45 0.156093 0.567663 0.16439 0.217317 0.032127 0.160675 0.156933 0.116744 0.08902 0.21533 -0.024783 -0.044777 0.021172 -0.00048 -0.138117 0.109935 0.12983

Benefi

ts

Career

Dev. a

nd

Trainin

g

Q46A 0.111454 0.179455 0.119372 0.784649 0.106111 0.117991 0.088349 0.09571 0.072149 -0.086474 0.108039 0.042973 0.041911 0.124074 0.04389 0.012272 0.132162Q46B 0.093091 0.178311 0.209987 0.812823 0.066673 0.02785 0.098464 0.10392 0.034562 0.005771 0.060292 0.019331 0.071737 0.029671 0.021827 0.017875 0.056875Q46C 0.094683 0.131634 0.223764 0.749899 0.081338 0.081484 0.10498 0.113307 0.06484 0.080766 0.073488 0.054103 0.038881 0.041188 0.119659 -0.004591 -0.03238Q47A 0.139444 0.130911 0.268451 0.673828 0.183156 0.111362 0.062655 0.136605 0.070539 0.234882 0.033908 0.062865 0.023642 0.102176 0.01961 0.059596 -0.034866Q47B 0.185991 0.168157 0.280524 0.66076 0.178382 0.07757 0.02442 0.079272 0.043562 0.196362 0.020626 0.066415 0.00636 0.077398 0.052183 0.03923 -0.039761Q48 0.115576 0.115753 0.262933 0.601172 0.118091 0.242326 0.035113 0.053256 0.002085 0.094609 0.169224 0.118737 0.032563 0.142444 0.015217 0.108074 -0.113727

Job C

onten

t

and S

atisf.

Career

Dev. a

nd

Trainin

g

Q49 0.080746 0.042518 0.186902 0.171112 0.096536 0.0704 0.088426 0.044582 0.216498 -0.021556 -0.048084 0.158817 0.05494 0.672145 0.072385 0.009402 -0.020215Q50 0.127252 0.060731 0.390533 0.324949 0.126809 0.085177 0.110001 0.095548 0.011296 0.157029 0.071103 0.067836 0.010896 0.482325 0.039714 0.095004 -0.075317Q51 0.12199 0.025257 0.267732 0.239713 0.155894 0.178788 0.039219 0.178512 -0.025793 0.063452 0.134358 0.184214 -0.036326 0.593174 0.063632 0.036553 0.037857Q52 0.171398 0.237158 0.486889 0.134535 0.035089 0.284621 0.110189 0.043794 0.156343 0.034919 0.0554 0.147581 0.012007 0.240392 -0.137417 -0.011768 -0.281278Jo

b Con

tent

and S

atisf.

Compa

nt Im

age a

nd

Commitm

ent

Q53 0.129701 0.158253 0.654401 0.255464 0.149197 0.118982 0.091441 0.218309 0.160487 0.055122 0.061285 0.091486 0.068378 0.048313 0.029119 0.010682 -0.04196Q54 0.200854 0.118098 0.629011 0.264954 0.187811 0.017297 0.097545 0.160723 0.044225 0.170867 0.077728 0.110211 0.084311 0.161752 0.055612 0.051484 -0.055084Q55 0.171895 0.12925 0.653991 0.179942 0.206355 0.139712 0.139766 0.070619 0.058211 -0.001039 0.103685 0.007093 0.069879 0.183231 0.058685 0.020019 0.088102Q56 0.111223 0.088664 0.797332 0.184796 0.143454 0.085669 0.10631 0.107987 0.081261 0.095747 0.065038 0.028484 0.071581 0.060325 0.059548 0.041496 0.032758Q57 0.104167 0.124505 0.772087 0.187555 0.181742 0.121594 0.134272 0.119845 0.06849 0.083858 0.04558 0.113188 0.049072 0.074767 0.049788 0.041503 0.041037Q58 0.246484 0.096754 0.581742 0.24824 0.111925 0.294007 0.024928 0.176573 -0.015041 0.115381 0.183198 0.121178 0.020721 0.069076 0.016535 0.062541 -0.077217Q59 0.18147 0.07477 0.647599 0.194584 0.094537 0.19967 0.036889 -0.035571 0.190137 0.090999 0.10767 0.152621 0.030986 0.05662 0.105006 0.004755 0.07908Extraction Method: Principal Component Analysis. � Rotation Method: Varimax with Kaiser Normalization. No Deletion: Mean Substitution.

Compa

nt Im

age a

nd

Commitm

ent

17

103

Appendix C

Table C1. English Sample � Missing Data Pattern.

The missing data fields are marked yellow.

Table C2. Spanish Sample � Missing Data Pattern.

The missing data fields are marked yellow.

104

Appendix D

Table D1. Five survey dimensions� item correlation matrix for the English sample.

Q1 Q2 Q3 Q4 Q5 Q6A Q6B Q7 Q8 Q9 Q10 Q11 Q12A Q12B Q12C Q13 Q14A Q14BQ1 1Q2 0.412 1Q3 0.524 0.504 1Q4 0.484 0.397 0.458 1Q5 0.379 0.607 0.511 0.529 1Q6A 0.311 0.373 0.553 0.408 0.353 1Q6B 0.277 0.438 0.53 0.328 0.393 0.825 1Q7 0.411 0.469 0.565 0.494 0.453 0.649 0.669 1Q8 0.318 0.447 0.563 0.382 0.44 0.673 0.684 0.582 1Q9 0.477 0.337 0.464 0.449 0.486 0.357 0.394 0.573 0.487 1Q10 0.488 0.317 0.601 0.46 0.528 0.535 0.546 0.659 0.631 0.648 1Q11 0.471 0.43 0.506 0.573 0.549 0.417 0.363 0.568 0.47 0.605 0.71 1Q12A 0.169 0.246 0.234 0.386 0.232 0.313 0.205 0.351 0.383 0.227 0.257 0.286 1Q12B 0.234 0.24 0.214 0.457 0.177 0.362 0.227 0.396 0.379 0.273 0.258 0.347 0.899 1Q12C 0.179 0.299 0.203 0.393 0.227 0.369 0.307 0.457 0.367 0.268 0.296 0.349 0.748 0.841 1Q13 0.176 0.418 0.431 0.163 0.276 0.523 0.599 0.469 0.587 0.313 0.403 0.217 0.171 0.204 0.292 1Q14A 0.18 0.297 0.323 0.336 0.328 0.448 0.489 0.482 0.514 0.272 0.333 0.322 0.197 0.219 0.171 0.335 1Q14B 0.281 0.357 0.385 0.316 0.303 0.481 0.531 0.449 0.519 0.305 0.37 0.314 0.274 0.294 0.264 0.407 0.695 1Q14C 0.199 0.232 0.265 0.202 0.218 0.471 0.509 0.437 0.512 0.342 0.405 0.208 0.288 0.311 0.326 0.438 0.4 0.638Q14D 0.202 0.341 0.332 0.236 0.303 0.452 0.432 0.398 0.462 0.274 0.398 0.265 0.232 0.274 0.317 0.603 0.386 0.504Q14E 0.344 0.39 0.539 0.362 0.325 0.577 0.585 0.5 0.615 0.394 0.499 0.352 0.436 0.417 0.395 0.514 0.342 0.461Q15 0.216 0.289 0.436 0.243 0.366 0.483 0.535 0.429 0.47 0.386 0.511 0.337 0.17 0.17 0.128 0.468 0.345 0.342Q16 0.256 0.39 0.333 0.191 0.325 0.464 0.473 0.408 0.38 0.334 0.438 0.328 0.116 0.157 0.156 0.597 0.312 0.373Q17 0.51 0.585 0.58 0.507 0.574 0.516 0.513 0.564 0.544 0.455 0.6 0.595 0.175 0.181 0.203 0.442 0.342 0.355Q18 0.459 0.291 0.33 0.422 0.369 0.218 0.216 0.28 0.266 0.432 0.436 0.392 0.18 0.198 0.088 0.077 0.035 0.087Q19 0.383 0.243 0.359 0.299 0.339 0.27 0.246 0.456 0.319 0.604 0.464 0.321 0.185 0.2 0.175 0.334 0.144 0.24Q20 0.504 0.37 0.488 0.46 0.423 0.38 0.369 0.54 0.41 0.546 0.563 0.467 0.229 0.291 0.227 0.357 0.282 0.413Q21 0.36 0.28 0.547 0.276 0.309 0.608 0.522 0.544 0.649 0.381 0.598 0.385 0.278 0.308 0.309 0.532 0.41 0.382Q22 0.216 0.026 0.143 0.239 0.153 0.175 0.163 0.115 0.18 0.159 0.158 0.154 0.175 0.186 0.079 0.13 0.134 0.2Q23 0.404 0.416 0.461 0.343 0.397 0.355 0.35 0.445 0.474 0.438 0.57 0.515 0.196 0.179 0.245 0.307 0.218 0.355Q24 0.412 0.319 0.406 0.356 0.321 0.409 0.332 0.531 0.439 0.393 0.531 0.483 0.188 0.237 0.23 0.265 0.286 0.363Q25 0.331 0.253 0.354 0.348 0.257 0.454 0.317 0.472 0.343 0.41 0.502 0.484 0.093 0.186 0.239 0.325 0.258 0.3Q26 0.322 0.401 0.402 0.367 0.383 0.451 0.37 0.477 0.443 0.365 0.462 0.501 0.149 0.2 0.227 0.351 0.362 0.354Q27A 0.41 0.395 0.525 0.294 0.311 0.663 0.609 0.57 0.674 0.393 0.551 0.437 0.345 0.368 0.338 0.491 0.442 0.518Q27B 0.324 0.189 0.398 0.091 0.14 0.43 0.402 0.326 0.389 0.308 0.31 0.236 0.149 0.228 0.226 0.382 0.204 0.277Q28 0.377 0.311 0.448 0.347 0.438 0.219 0.201 0.438 0.374 0.484 0.527 0.476 0.156 0.132 0.064 0.286 0.108 0.095Q29 0.306 0.219 0.333 0.14 0.295 0.188 0.157 0.361 0.353 0.424 0.432 0.288 0.109 0.073 0.016 226 0.124 0.076Q30 0.356 0.299 0.422 0.1 0.242 0.388 0.441 0.372 0.528 0.362 0.468 0.263 0.13 0.15 0.156 618 0.202 0.303Q31A 0.404 0.221 0.37 0.306 0.273 0.34 0.285 0.343 0.314 0.299 0.464 0.432 0.205 0.189 0.211 198 0.152 0.174Q31B 0.316 0.295 0.36 0.465 0.468 0.384 0.328 0.43 0.361 0.364 0.523 0.447 0.235 0.257 0.354 0.271 0.183 0.28

0.416 0.102 0.092 0.224 0.26 0.164 0.2120.458 0.147 0.11 0.165 0.377 0.203 0.2760.313 0.197 0.153 0.141 0.381 0.06 0.148

Q31F 0.467 0.405 0.462 0.5 0.524 0.475 0.444 0.469 0.473 0.519 0.592 0.556 0.172 0.173 0.233 0.369 0.272 0.328465 0.369 0.328 0.535 0.432 0.276 0.264 0.269 0.292 0.187 0.279154 0.236 0.274 0.202 0.297 0.185 0.293 0.126 0.159 0.132 0.219

Q32B 0.215 0.42 0.303 0.323 0.27 0.333 0.329 0.254 0.344 0.232 0.289 0.342 0.319 0.37 0.321 0.39 0.304 0.385Q32C 0.364 0.459 0.382 0.253 0.308 0.442 0.431 0.331 0.466 0.31 0.443 0.411 0.327 0.377 0.403 0.432 0.183 0.333Q32D 0.418 0.388 0.428 0.48 0.342 0.561 0.484 0.479 0.538 0.37 0.559 0.536 0.383 0.428 0.366 0.377 0.368 0.556Q33 0.316 0.255 0.442 0.334 0.313 0.574 0.538 0.419 0.565 0.31 0.554 0.516 0.137 0.169 0.208 0.461 0.334 0.303Q34 0.378 0.456 0.355 0.383 0.429 0.513 0.482 0.461 0.581 0.345 0.471 0.466 0.291 0.316 0.319 0.466 0.326 0.331Q35 0.32 0.329 0.38 0.369 0.344 0.54 0.483 0.416 0.525 0.254 0.411 0.421 0.309 0.334 0.368 0.388 0.499 0.407Q36 0.341 0.413 0.433 0.346 0.316 0.627 0.557 0.432 0.564 0.24 0.403 0.427 0.354 0.364 0.366 0.508 0.437 0.446Q37A 0.244 0.211 0.395 0.27 0.331 0.384 0.329 0.383 0.348 0.273 0.397 0.3 0.125 0.15 0.102 0.31 0.459 0.337Q37B 0.184 0.295 0.337 0.157 0.276 0.308 0.278 0.274 0.378 0.242 0.306 0.301 0.078 0.126 0.049 0.29 0.502 0.377Q38 0.31 0.339 0.453 0.313 0.261 0.563 0.562 0.474 0.502 0.281 0.414 0.375 0.206 0.259 0.258 0.462 0.523 0.511Q39 0.467 0.395 0.512 0.439 0.383 0.572 0.54 0.639 0.518 0.479 0.631 0.6 0.259 0.318 0.297 0.423 0.418 0.43Q40 0.28 0.299 0.381 0.303 0.322 0.409 0.308 0.421 0.402 0.367 0.347 0.372 0.201 0.213 0.149 0.25 0.433 0.347Q41 0.218 0.182 0.169 0.258 0.152 0.283 0.265 0.327 0.227 0.23 0.263 0.39 0.098 0.199 0.191 0.083 0.256 0.336Q42 0.323 0.226 0.254 0.274 0.283 0.391 0.395 0.328 0.426 0.341 0.429 0.406 0.242 0.328 0.291 0.299 0.399 0.483Q43A 0.21 0.186 0.262 0.256 0.12 0.375 0.343 0.313 0.324 0.274 0.298 0.352 0.173 0.244 0.193 0.216 0.328 0.459Q43B 0.211 0.18 0.204 0.163 0.149 0.422 0.382 0.349 0.298 0.267 0.263 0.361 0.092 0.166 0.255 0.26 0.242 0.358Q43C 0.143 0.128 0.09 0.019 0.056 0.196 0.208 0.16 0.129 0.091 0.094 0.064 -0.026 -0.006 0.075 0.291 0.233 0.357Q43D 0.184 0.169 0.208 0.189 0.067 0.274 0.28 0.195 0.215 0.108 0.136 0.068 0.097 0.156 0.096 0.223 0.265 0.406Q43E 0.31 0.309 0.288 0.37 0.259 0.383 0.338 0.264 0.365 0.218 0.206 0.271 0.258 0.364 0.314 0.192 0.322 0.462Q43F 0.349 0.243 0.24 0.251 0.221 0.285 0.246 0.246 0.358 0.234 0.245 0.212 0.322 0.373 0.265 0.134 0.324 0.41Q43G 0.322 0.273 0.214 0.248 0.207 0.287 0.17 0.246 0.29 0.159 0.214 0.261 0.345 0.395 0.31 0.164 0.237 0.279Q43H 0.144 0.223 0.11 0.052 0.07 0.288 0.248 0.295 0.22 0.119 0.186 0.154 0.172 0.242 0.215 0.263 0.179 0.236Q44 0.327 0.235 0.252 0.222 0.168 0.372 0.346 0.241 0.332 0.184 0.229 0.297 0.157 0.206 0.188 0.258 0.375 0.489Q45 0.148 0.137 0.204 0.052 0.083 0.219 0.261 0.141 0.164 0.088 0.152 0.128 0.015 0.046 0.134 0.37 0.221 0.296Q46A 0.274 0.294 0.375 0.392 0.367 0.368 0.424 0.385 0.486 0.388 0.457 0.342 0.32 0.326 0.304 0.312 0.244 0.314Q46B 0.389 0.287 0.455 0.404 0.367 0.457 0.45 0.426 0.459 0.364 0.573 0.415 0.258 0.29 0.203 0.268 0.293 0.359Q46C 0.373 0.462 0.501 0.418 0.418 0.59 0.555 0.535 0.539 0.411 0.531 0.522 0.288 0.338 0.312 0.502 0.364 0.308Q47A 0.42 0.339 0.434 0.438 0.478 0.389 0.445 0.524 0.452 0.554 0.573 0.448 0.23 0.232 0.295 0.423 0.307 0.354Q47B 0.355 0.4 0.327 0.306 0.383 0.26 0.341 0.413 0.329 0.447 0.349 0.295 0.119 0.161 0.312 0.394 0.104 0.105Q48 0.325 0.426 0.474 0.519 0.475 0.455 0.471 0.527 0.448 0.454 0.467 0.417 0.321 0.321 0.34 0.355 0.2 0.274Q49 0.542 0.277 0.381 0.453 0.394 0.134 0.138 0.472 0.236 0.461 0.568 0.575 0.357 0.359 0.275 0.145 0.072 0.119Q50 0.433 0.233 0.25 0.388 0.301 0.213 0.201 0.416 0.29 0.328 0.489 0.475 0.226 0.268 0.149 0.1 0.225 0.116Q51 0.364 0.364 0.279 0.376 0.378 0.26 0.244 0.457 0.303 0.396 0.443 0.454 0.23 0.319 0.272 0.251 0.272 0.279Q52 0.439 0.295 0.396 0.42 0.379 0.457 0.392 0.583 0.421 0.369 0.635 0.588 0.274 0.316 0.352 0.287 0.328 0.306Q53 0.3 0.387 0.424 0.223 0.344 0.57 0.564 0.579 0.558 0.382 0.524 0.45 0.355 0.369 0.415 0.495 0.45 0.481Q54 0.416 0.416 0.368 0.412 0.324 0.604 0.553 0.568 0.534 0.412 0.406 0.43 0.392 0.458 0.409 0.479 0.407 0.423Q55 0.437 0.36 0.409 0.396 0.345 0.509 0.459 0.6 0.498 0.494 0.622 0.556 0.354 0.428 0.386 0.44 0.216 0.355Q56 0.374 0.468 0.409 0.387 0.347 0.539 0.495 0.64 0.498 0.47 0.494 0.501 0.405 0.475 0.44 0.439 0.357 0.457Q57 0.421 0.393 0.48 0.312 0.352 0.547 0.504 0.625 0.576 0.43 0.603 0.473 0.42 0.426 0.391 0.476 0.391 0.375Q58 0.413 0.429 0.456 0.347 0.374 0.53 0.498 0.646 0.549 0.438 0.575 0.522 0.444 0.475 0.454 0.426 0.38Q59 0.283 0.352 0.401 0.261 0.306 0.548 0.579 0.43 0.55 0.321 0.455 0.374 0.373 0.39 0.401 0.427 0.535

0.0.0.

Q31C 0.345 0.235 0.361 0.258 0.277 0.323 0.295 0.362 0.287 0.375 0.523Q31D 0.409 0.323 0.509 0.387 0.439 0.434 0.457 0.443 0.369 0.421 0.557Q31E 0.394 0.359 0.402 0.321 0.397 0.306 0.341 0.389 0.392 0.365 0.456

Q31G 0.394 0.27 0.454 0.371 0.393 0.42 0.355 0.Q32A 0.273 0.179 0.123 0.294 0.15 0.308 0.269 0.

0.4590.536

105

106

Q14C Q14D Q14E Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27A Q27B Q28Q14C 1Q14D 0.544 1Q14E 0.506 0.639 1Q15 0.351 0.434 0.495 1

Q19 0.301 0.268 0.312 0.272 0.346 0.432 0.401 1Q20 0.303 0.366 0.432 0.38 0.367 0.516 0.462 0.689 1Q21 0.387 0.497 0.614 0.559 0.484 0.511 0.305 0.418 0.47 1Q22 0.043 0.145 0.194 0.247 0.063 0.211 0.537 0.36 0.334 0.38 1Q23 0.358 0.382 0.455 0.209 0.311 0.516 0.384 0.462 0.44 0.456 0.179 1Q24 0.303 0.297 0.36 0.246 0.275 0.495 0.46 0.432 0.438 0.578 0.27 0.715 1Q25 0.283 0.357 0.339 0.245 0.317 0.402 0.324 0.429 0.394 0.49 0.153 0.525 0.602 1Q26 0.225 0.405 0.393 0.315 0.349 0.48 0.33 0.437 0.455 0.486 0.204 0.61 0.641 0.776 1Q27A 0.41 0.486 0.593 0.447 0.378 0.511 0.268 0.386 0.41 0.696 0.275 0.476 0.547 0.503 0.538 1Q27B 0.179 0.373 0.375 0.318 0.338 0.404 0.238 0.233 0.31 0.49 0.338 0.254 0.336 0.335 0.313 0.56 1Q28 0.118 0.232 0.355 0.33 0.21 0.468 0.498 0.46 0.517 0.399 0.273 0.451 0.516 0.423 0.524 0.351 0.322 1Q29 0.118 0.11 0.153 0.181 0.11 0.342 0.424 0.467 0.393 0.289 0.184 0.364 0.462 0.373 0.382 0.325 0.332 0.719Q30 0.379 0.347 0.45 0.403 0.444 0.393 0.22 0.345 0.387 0.551 0.176 0.293 0.314 0.283 0.311 0.466 0.317 0.382Q31A 0.125 0.104 0.219 0.143 0.215 0.423 0.43 0.313 0.237 0.34 0.197 0.491 0.607 0.517 0.47 0.484 0.31 0.414Q31B 0.256 0.278 0.224 0.214 0.246 0.46 0.389 0.31 0.362 0.345 0.188 0.49 0.455 0.532 0.438 0.443 0.255 0.337Q31C 0.27 0.253 0.287 0.164 0.285 0.424 0.361 0.341 0.329 0.333 0.157 0.479 0.45 0.579 0.436 0.357 0.234 0.39Q31D 0.236 0.333 0.345 0.386 0.394 0.561 0.503 0.391 0.422 0.441 0.292 0.483 0.479 0.549 0.536 0.498 0.442 0.492Q31E 0.193 0.231 0.338 0.339 0.244 0.47 0.553 0.414 0.414 0.403 0.335 0.452 0.512 0.453 0.5 0.427 0.277 0.633Q31F 0.236 0.354 0.363 0.345 0.399 0.636 0.516 0.402 0.454 0.485 0.323 0.504 0.457 0.541 0.533 0.497 0.378 0.434Q31G 0.227 0.227 0.326 0.338 0.372 0.568 0.523 0.391 0.452 0.513 0.325 0.464 0.567 0.549 0.531 0.451 0.354 0.425Q32A 0.164 0.399 0.393 0.399 0.307 0.425 0.342 0.196 0.275 0.311 0.293 0.087 0.178 0.255 0.283 0.358 0.394 0.276Q32B 0.367 0.463 0.471 0.451 0.447 0.474 0.111 0.278 0.336 0.392 0.092 0.224 0.181 0.231 0.268 0.439 0.315 0.215Q32C 0.402 0.519 0.602 0.397 0.414 0.472 0.215 0.294 0.342 0.412 0.148 0.401 0.284 0.231 0.298 0.448 0.249 0.285Q32D 0.465 0.45 0.567 0.443 0.431 0.505 0.282 0.274 0.413 0.49 0.183 0.323 0.341 0.39 0.393 0.596 0.302 0.297Q33 0.25 0.388 0.523 0.404 0.496 0.557 0.27 0.2 0.257 0.623 0.258 0.39 0.423 0.422 0.432 0.566 0.416 0.3Q34 0.322 0.487 0.576 0.428 0.483 0.51 0.325 0.292 0.365 0.542 0.25 0.348 0.323 0.347 0.398 0.54 0.369 0.339Q35 0.247 0.442 0.502 0.394 0.503 0.486 0.225 0.158 0.305 0.556 0.201 0.321 0.359 0.309 0.429 0.526 0.385 0.215Q36 0.394 0.548 0.622 0.538 0.481 0.492 0.231 0.19 0.358 0.612 0.242 0.342 0.343 0.302 0.413 0.613 0.348 0.234Q37A 0.201 0.259 0.295 0.303 0.227 0.266 0.162 0.3 0.277 0.407 0.138 0.295 0.377 0.35 0.351 0.462 0.253 0.295Q37B 0.296 0.322 0.282 0.288 0.206 0.251 0.201 0.24 0.3 0.326 0.146 0.339 0.342 0.366 0.445 0.368 0.178 0.286Q38 0.333 0.361 0.507 0.419 0.447 0.438 0.191 0.307 0.321 0.553 0.174 0.391 0.399 0.392 0.465 0.544 0.342 0.194Q39 0.346 0.337 0.464 0.435 0.345 0.482 0.407 0.446 0.515 0.572 0.253 0.438 0.593 0.581 0.529 0.601 0.327 0.455Q40 0.239 0.225 0.328 0.225 0.174 0.296 0.246 0.335 0.349 0.405 0.104 0.454 0.511 0.443 0.48 0.467 0.143 0.243Q41 0.271 0.243 0.234 0.236 0.214 0.287 0.22 0.166 0.175 0.284 0.209 0.371 0.415 0.402 0.295 0.286 0.24 0.076Q42 0.371 0.391 0.462 0.333 0.369 0.381 0.215 0.269 0.428 0.487 0.226 0.329 0.371 0.394 0.372 0.448 0.334 0.145Q43A 0.353 0.321 0.369 0.353 0.338 0.345 0.107 0.21 0.232 0.393 0.164 0.404 0.454 0.425 0.437 0.43 0.311 0.191Q43B 0.337 0.326 0.268 0.23 0.329 0.302 0.028 0.18 0.132 0.378 0.092 0.459 0.443 0.442 0.432 0.396 0.333 0.146Q43C 0.312 0.343 0.195 0.183 0.418 0.267 -0.06 0.191 0.191 0.286 0.095 0.218 0.182 0.238 0.271 0.216 0.230 0.005Q43D 0.286 0.451 0.371 0.213 0.329 0.239 0.159 0.179 0.326 0.314 0.203 0.258 0.285 0.343 0.418 0.371 0.394 0.162Q43E 0.253 0.395 0.306 0.2 0.328 0.381 0.267 0.158 0.328 0.346 0.205 0.361 0.39 0.357 0.389 0.413 0.412 0.114Q43F 0.28 0.287 0.31 0.16 0.131 0.274 0.317 0.24 0.375 0.353 0.243 0.324 0.375 0.301 0.327 0.397 0.419 0.162Q43G 0.279 0.366 0.251 0.176 0.191 0.295 0.198 0.25 0.323 0.301 0.172 0.229 0.274 0.255 0.251 0.367 0.353 0.142Q43H 0.128 0.169 0.119 0.153 0.21 0.129 0.135 0.111 0.195 0.19 0.102 0.048 0.192 0.11 0.175 0.265 0.253 0.188Q44 0.343 0.261 0.277 0.276 0.375 0.358 0.215 0.196 0.218 0.303 0.264 0.355 0.294 0.327 0.327 0.357 0.285 0.128Q45 0.321 0.346 0.202 0.249 0.418 0.252 0.019 0.181 0.115 0.278 0.177 0.316 0.217 0.233 0.268 0.226 0.214 0.005Q46A 0.398 0.376 0.52 0.384 0.198 0.313 0.291 0.299 0.417 0.387 0.079 0.229 0.287 0.324 0.316 0.463 0.261 0.338Q46B 0.326 0.319 0.492 0.407 0.34 0.421 0.353 0.328 0.484 0.472 0.161 0.329 0.418 0.41 0.424 0.523 0.334 0.377Q46C 0.289 0.369 0.548 0.438 0.434 0.526 0.3 0.367 0.415 0.52 0.151 0.326 0.349 0.447 0.522 0.626 0.397 0.368Q47A 0.447 0.341 0.482 0.418 0.393 0.428 0.313 0.581 0.443 0.434 0.25 0.463 0.405 0.483 0.481 0.45 0.228 0.341Q47B 0.273 0.246 0.328 0.255 0.295 0.4 0.22 0.408 0.283 0.325 0.08 0.345 0.3 0.413 0.328 0.304 0.235 0.265Q48 0.295 0.317 0.465 0.418 0.344 0.413 0.334 0.355 0.392 0.405 0.098 0.364 0.367 0.411 0.44 0.487 0.188 0.316Q49 0.186 0.107 0.239 0.257 0.255 0.435 0.574 0.498 0.537 0.356 0.293 0.39 0.489 0.398 0.389 0.283 0.161 0.583Q50 0.116 0.16 0.235 0.374 0.252 0.444 0.582 0.368 0.425 0.404 0.408 0.323 0.514 0.324 0.432 0.268 0.159 0.495Q51 0.164 0.284 0.24 0.365 0.358 0.451 0.443 0.346 0.43 0.342 0.216 0.307 0.475 0.459 0.503 0.278 0.226 0.385Q52 0.249 0.309 0.355 0.332 0.267 0.459 0.355 0.337 0.436 0.508 0.225 0.456 0.613 0.575 0.575 0.54 0.196 0.402Q53 0.376 0.478 0.558 0.503 0.422 0.449 0.282 0.332 0.475 0.614 0.269 0.475 0.467 0.414 0.504 0.629 0.376 0.314Q54 0.318 0.383 0.517 0.328 0.433 0.444 0.294 0.349 0.482 0.571 0.225 0.343 0.394 0.469 0.505 0.561 0.369 0.308Q55 0.369 0.446 0.516 0.355 0.391 0.456 0.45 0.475 0.549 0.549 0.218 0.436 0.499 0.553 0.517 0.575 0.253 0.424Q56 0.358 0.487 0.55 0.437 0.433 0.445 0.343 0.386 0.524 0.5 0.181 0.378 0.441 0.466 0.486 0.574 0.312 0.367Q57 0.43 0.475 0.635 0.464 0.479 0.446 0.293 0.435 0.499 0.686 0.188 0.508 0.519 0.463 0.54 0.676 0.368 0.473Q58 0.401 0.443 0.603 0.457 0.472 0.507 0.259 0.408 0.495 0.615 0.143 0.422 0.436 0.397 0.454 0.589 0.303 0.41Q59 0.454 0.471 0.605 0.484 0.443 0.429 0.234 0.315 0.463 0.592 0.29 0.392 0.44 0.346 0.453 0.559 0.271 0.242

Q29 Q30 Q31A Q31B Q31C Q31D Q31E Q31F Q31G Q32A Q32B Q32C Q32D Q33 Q34 Q35 Q36 Q37AQ29 1Q30 0.329 1Q31A 0.434 0.347 1Q31B 0.293 0.279 0.742 1Q31C 0.422 0.349 0.737 0.748 1Q31D 0.397 0.381 0.726 0.74 0.72 1Q31E 0.524 0.522 0.641 0.618 0.614 0.704 1Q31F 0.337 0.385 0.614 0.767 0.715 0.814 0.662 1Q31G 0.395 0.446 0.702 0.719 0.722 0.79 0.711 0.771 1Q32A 0.067 0.119 0.187 0.129 0.092 0.246 0.2 0.291 0.21 1Q32B 0.088 0.309 0.22 0.217 0.161 0.263 0.218 0.287 0.263 0.559 1Q32C 0.144 0.397 0.259 0.311 0.331 0.293 0.272 0.383 0.314 0.45 0.598 1Q32D 0.098 0.387 0.309 0.457 0.275 0.389 0.33 0.469 0.41 0.416 0.602 0.627 1Q33 0.123 0.442 0.391 0.44 0.378 0.445 0.333 0.569 0.468 0.378 0.358 0.456 0.561 1Q34 0.108 0.451 0.235 0.314 0.214 0.344 0.328 0.486 0.36 0.468 0.491 0.569 0.633 0.717 1Q35 0.109 0.322 0.318 0.32 0.279 0.387 0.216 0.458 0.409 0.412 0.489 0.461 0.46 0.728 0.689 1Q36 0.073 0.41 0.293 0.302 0.248 0.335 0.315 0.423 0.37 0.481 0.537 0.598 0.588 0.641 0.693 0.765 1Q37A 0.261 0.263 0.443 0.359 0.297 0.394 0.307 0.37 0.353 0.092 0.325 0.206 0.376 0.291 0.311 0.351 0.347 1Q37B 0.304 0.306 0.289 0.251 0.303 0.316 0.318 0.32 0.361 0.11 0.378 0.286 0.336 0.247 0.262 0.313 0.368 0.746Q38 0.1 0.377 0.326 0.317 0.243 0.376 0.299 0.413 0.408 0.249 0.485 0.399 0.597 0.541 0.509 0.589 0.603 0.635Q39 0.379 0.384 0.547 0.499 0.455 0.534 0.476 0.573 0.542 0.257 0.374 0.425 0.614 0.454 0.466 0.451 0.524 0.65Q40 0.347 0.283 0.452 0.407 0.38 0.372 0.385 0.421 0.451 0.039 0.299 0.216 0.365 0.275 0.243 0.321 0.379 0.746

Q16 0.405 0.456 0.463 0.391 1Q17 0.24 0.447 0.508 0.498 0.55 1Q18 -0.018 0.034 0.144 0.317 0.109 0.417 1

107

107

Q29 Q30 Q31A Q31B Q31C Q31D Q31E Q31F Q31G Q32A Q32B Q32C Q32D Q33 Q34 Q35 Q36 Q37AQ41 0.135 0.098 0.209 0.168 0.156 0.238 0.088 0.281 0.264 0.181 0.2 0.216 0.194 0.236 0.192 0.249 0.207 0.2Q42 0.089 0.422 0.213 0.22 0.273 0.256 0.15 0.403 0.403 0.262 0.323 0.39 0.449 0.375 0.464 0.431 0.441 0.349Q43A 0.204 0.215 0.246 0.187 0.179 0.282 0.205 0.316 0.311 0.268 0.362 0.361 0.368 0.328 0.27 0.363 0.361 0.272Q43B 0.129 0.219 0.227 0.226 0.21 0.315 0.168 0.333 0.255 0.165 0.232 0.268 0.224 0.335 0.26 0.294 0.228 0.181Q43C 0.03 0.272 0.084 0.138 0.166 0.248 0.171 0.259 0.239 0.104 0.188 0.141 0.128 0.145 0.099 0.268 0.224 0.104Q43D 0.05 0.237 0.107 0.111 0.086 0.287 0.188 0.271 0.257 0.318 0.401 0.271 0.377 0.209 0.366 0.35 0.285 0.235Q43E 0.053 0.192 0.201 0.28 0.157 0.32 0.173 0.392 0.323 0.316 0.39 0.355 0.428 0.286 0.43 0.43 0.322 0.231Q43F 0.247 0.187 0.206 0.195 0.162 0.238 0.189 0.32 0.296 0.232 0.331 0.285 0.379 0.149 0.31 0.235 0.206 0.332Q43G 0.206 0.114 0.198 0.211 0.191 0.193 0.146 0.252 0.235 0.276 0.387 0.311 0.34 0.107 0.353 0.253 0.259 0.219Q43H 0.244 0.29 0.085 0.107 0.052 0.089 0.166 0.111 0.101 0.026 0.172 0.229 0.273 0.134 0.202 0.2 0.216 0.283Q44 0.224 0.261 0.314 0.247 0.268 0.38 0.249 0.363 0.381 0.257 0.342 0.349 0.374 0.291 0.283 0.414 0.373 0.33Q45 0.103 0.361 0.173 0.126 0.196 0.308 0.153 0.203 0.269 0.014 0.283 0.225 0.14 0.201 0.072 0.255 0.262 0.176Q46A 0.196 0.392 0.18 0.259 0.205 0.308 0.379 0.356 0.303 0.264 0.332 0.306 0.469 0.41 0.526 0.322 0.391 0.233Q46B 0.285 0.416 0.336 0.323 0.347 0.421 0.432 0.45 0.477 0.325 0.36 0.287 0.488 0.496 0.469 0.454 0.426 0.355Q46C 0.315 0.492 0.385 0.396 0.34 0.448 0.41 0.486 0.438 0.37 0.455 0.461 0.57 0.654 0.616 0.589 0.649 0.398Q47A 0.266 0.45 0.362 0.41 0.439 0.462 0.501 0.522 0.449 0.2 0.371 0.355 0.398 0.425 0.524 0.39 0.404 0.335Q47B 0.196 0.452 0.324 0.394 0.394 0.394 0.46 0.454 0.428 0.151 0.283 0.251 0.194 0.371 0.473 0.334 0.303 0.178Q48 0.222 0.386 0.366 0.444 0.341 0.421 0.495 0.483 0.472 0.259 0.356 0.355 0.431 0.455 0.509 0.452 0.494 0.314Q49 0.349 0.333 0.436 0.403 0.362 0.462 0.537 0.409 0.477 0.247 0.289 0.286 0.468 0.32 0.364 0.226 0.203 0.179Q50 0.295 0.304 0.422 0.302 0.325 0.427 0.494 0.387 0.534 0.361 0.305 0.294 0.389 0.33 0.354 0.351 0.33 0.259Q51 0.271 0.37 0.336 0.342 0.315 0.416 0.444 0.385 0.519 0.29 0.349 0.255 0.416 0.302 0.343 0.361 0.253 0.194Q52 0.299 0.376 0.559 0.533 0.488 0.518 0.515 0.515 0.6 0.226 0.369 0.415 0.564 0.504 0.413 0.488 0.5 0.505Q53 0.277 0.454 0.375 0.379 0.336 0.465 0.38 0.436 0.483 0.235 0.449 0.498 0.524 0.513 0.516 0.618 0.686 0.469Q54 0.193 0.34 0.308 0.321 0.266 0.356 0.352 0.435 0.374 0.379 0.428 0.424 0.514 0.514 0.561 0.617 0.602 0.327Q55 0.311 0.477 0.432 0.459 0.424 0.446 0.564 0.505 0.543 0.377 0.405 0.544 0.63 0.488 0.6 0.409 0.565 0.41Q56 0.232 0.381 0.322 0.379 0.253 0.388 0.4 0.396 0.386 0.443 0.522 0.551 0.64 0.449 0.613 0.546 0.652 0.353Q57 0.368 0.54 0.439 0.391 0.403 0.471 0.483 0.437 0.53 0.315 0.474 0.53 0.538 0.533 0.568 0.574 0.669 0.476Q58 0.323 0.514 0.393 0.384 0.424 0.437 0.423 0.464 0.511 0.349 0.508 0.545 0.569 0.555 0.587 0.614 0.636 0.463Q59 0.126 0.581 0.359 0.344 0.337 0.463 0.454 0.452 0.48 0.26 0.445 0.492 0.513 0.485 0.428 0.517 0.639 0.396

Q37B Q38 Q39 Q40 Q41 Q42 Q43A Q43B Q43C Q43D Q43E Q43F Q43G Q43H Q44 Q45 Q46A Q46BQ37B 1Q38 0.605 1Q39 0.515 0.65 1Q40 0.748 0.618 0.661 1Q41 0.265 0.324 0.339 0.285 1Q42 0.351 0.516 0.566 0.396 0.486 1Q43A 0.303 0.453 0.41 0.324 0.768 0.542 1Q43B 0.149 0.303 0.278 0.235 0.649 0.339 0.734 1Q43C 0.129 0.277 0.22 0.14 0.363 0.418 0.521 0.455 1Q43D 0.287 0.389 0.325 0.254 0.316 0.529 0.474 0.346 0.463 1Q43E 0.24 0.41 0.366 0.302 0.461 0.576 0.544 0.456 0.393 0.695 1Q43F 0.356 0.329 0.406 0.422 0.385 0.541 0.429 0.343 0.286 0.617 0.744 1Q43G 0.192 0.187 0.336 0.256 0.286 0.401 0.303 0.317 0.238 0.471 0.601 0.751 1Q43H 0.343 0.303 0.325 0.295 0.215 0.246 0.273 0.17 0.135 0.267 0.252 0.293 0.317 1Q44 0.376 0.457 0.438 0.383 0.543 0.487 0.664 0.537 0.569 0.49 0.528 0.507 0.423 0.237 1Q45 0.286 0.296 0.192 0.217 0.473 0.342 0.528 0.489 0.597 0.374 0.316 0.232 0.195 0.174 0.655 1Q46A 0.251 0.302 0.449 0.215 0.25 0.457 0.305 0.21 0.106 0.363 0.335 0.409 0.303 0.165 0.152 0.071 1Q46B 0.352 0.389 0.523 0.349 0.253 0.47 0.358 0.282 0.14 0.441 0.344 0.443 0.326 0.219 0.277 0.127 0.804 1Q46C 0.385 0.574 0.61 0.436 0.189 0.415 0.339 0.254 0.136 0.241 0.218 0.234 0.186 0.277 0.27 0.16 0.575 0.66Q47A 0.244 0.422 0.568 0.345 0.268 0.46 0.331 0.342 0.271 0.272 0.265 0.267 0.228 0.069 0.286 0.28 0.566 0.55Q47B 0.143 0.21 0.319 0.188 0.173 0.279 0.183 0.283 0.172 0.13 0.166 0.141 0.186 0.108 0.18 0.282 0.434 0.366Q48 0.215 0.354 0.551 0.357 0.173 0.393 0.305 0.245 0.157 0.309 0.311 0.227 0.213 0.117 0.182 0.128 0.627 0.62Q49 0.158 0.262 0.471 0.185 0.192 0.234 0.157 0.108 0.024 0.141 0.193 0.231 0.219 0.139 0.135 0.095 0.384 0.419Q50 0.318 0.348 0.467 0.251 0.196 0.256 0.175 0.07 0.062 0.183 0.149 0.178 0.198 0.172 0.212 0.122 0.268 0.395Q51 0.285 0.327 0.44 0.283 0.264 0.35 0.247 0.227 0.162 0.324 0.317 0.282 0.251 0.141 0.33 0.233 0.286 0.403Q52 0.459 0.5 0.693 0.544 0.224 0.371 0.281 0.255 0.121 0.224 0.235 0.221 0.185 0.259 0.282 0.203 0.26 0.448Q53 0.508 0.619 0.553 0.515 0.331 0.44 0.419 0.392 0.188 0.321 0.318 0.304 0.186 0.285 0.431 0.338 0.315 0.447Q54 0.276 0.573 0.604 0.42 0.245 0.543 0.386 0.377 0.282 0.394 0.398 0.376 0.32 0.229 0.381 0.145 0.403 0.468Q55 0.397 0.528 0.664 0.493 0.199 0.444 0.284 0.23 0.168 0.312 0.349 0.348 0.308 0.265 0.28 0.126 0.47 0.497Q56 0.37 0.55 0.616 0.398 0.237 0.377 0.343 0.269 0.152 0.372 0.389 0.294 0.277 0.29 0.31 0.159 0.425 0.435Q57 0.484 0.59 0.596 0.515 0.221 0.399 0.32 0.294 0.154 0.291 0.28 0.298 0.255 0.274 0.292 0.277 0.4 0.524Q58 0.457 0.572 0.647 0.523 0.184 0.458 0.321 0.241 0.24 0.284 0.336 0.347 0.298 0.321 0.374 0.271 0.377 0.505Q59 0.467 0.54 0.589 0.472 0.242 0.516 0.408 0.308 0.356 0.325 0.336 0.28 0.14 0.334 0.426 0.455 0.414 0.454

Q46C Q47A Q47B Q48 Q49 Q50 Q51 Q52 Q53 Q54 Q55 Q56 Q57 Q58 Q59Q46C 1Q47A 0.546 1Q47B 0.434 0.667 1Q48 0.66 0.682 0.559 1Q49 0.353 0.485 0.338 0.379 1Q50 0.329 0.38 0.251 0.326 0.71 1Q51 0.321 0.407 0.353 0.439 0.629 0.724 1Q52 0.501 0.495 0.321 0.498 0.578 0.689 0.603 1Q53 0.641 0.436 0.311 0.483 0.323 0.394 0.435 0.624 1Q54 0.662 0.484 0.399 0.581 0.348 0.353 0.44 0.493 0.648 1Q55 0.616 0.547 0.38 0.589 0.558 0.519 0.501 0.716 0.631 0.633 1Q56 0.623 0.515 0.37 0.61 0.484 0.473 0.569 0.632 0.746 0.705 0.798 1Q57 0.658 0.558 0.422 0.562 0.477 0.516 0.454 0.677 0.82 0.644 0.749 0.771 1Q58 0.665 0.507 0.448 0.526 0.491 0.467 0.458 0.658 0.718 0.617 0.711 0.721 0.859 1Q59 0.524 0.469 0.326 0.472 0.314 0.364 0.383 0.578 0.658 0.537 0.522 0.579 0.622 0.66 1

Table D2. Five survey dimensions� item correlation matrix for the Spanish sample.

Q1 Q2 Q3 Q4 Q5 Q6A Q6B Q7 Q8 Q9 Q10 Q11 Q12A Q12B Q12C Q13 Q14A Q14B

Q1 1Q2 0.675 1Q3 0.599 0.805 1Q4 0.564 0.741 0.784 1Q5 0.598 0.648 0.639 0.75 1Q6A 0.623 0.667 0.683 0.654 0.507 1Q6B 0.516 0.673 0.683 0.701 0.642 0.772 1Q7 0.472 0.459 0.558 0.555 0.472 0.468 0.476 1Q8 0.436 0.539 0.491 0.578 0.484 0.551 0.617 0.707 1Q9 0.367 0.529 0.518 0.46 0.355 0.424 0.449 0.318 0.45 1Q10 0.426 0.466 0.416 0.467 0.45 0.46 0.362 0.561 0.565 0.375 1Q11 0.593 0.613 0.626 0.687 0.534 0.638 0.569 0.439 0.491 0.397 0.41 1Q12A 0.517 0.601 0.601 0.568 0.42 0.631 0.53 0.475 0.534 0.424 0.365 0.628 1Q12B 0.558 0.628 0.645 0.601 0.395 0.666 0.513 0.538 0.525 0.437 0.405 0.701 0.869 1Q12C 0.659 0.631 0.616 0.605 0.398 0.652 0.525 0.555 0.543 0.404 0.39 0.729 0.82 0.898 1Q13 0.574 0.666 0.681 0.696 0.656 0.645 0.645 0.561 0.553 0.428 0.537 0.541 0.468 0.494 0.576 1Q14A 0.437 0.591 0.436 0.395 0.408 0.579 0.503 0.449 0.469 0.357 0.297 0.252 0.425 0.482 0.427 0.422 1Q14B 0.327 0.338 0.135 0.243 0.416 0.322 0.322 0.255 0.378 0.131 0.402 0.225 0.16 0.184 0.201 0.349 0.621 1Q14C 0.532 0.647 0.518 0.461 0.424 0.538 0.53 0.631 0.668 0.424 0.572 0.465 0.452 0.513 0.473 0.478 0.63 0.504Q14D 0.431 0.603 0.552 0.568 0.566 0.575 0.465 0.529 0.506 0.315 0.739 0.483 0.443 0.436 0.386 0.573 0.454 0.458Q14E 0.449 0.673 0.724 0.594 0.441 0.591 0.522 0.5 0.478 0.468 0.476 0.47 0.609 0.572 0.548 0.48 0.526 0.284Q15 0.592 0.585 0.565 0.595 0.447 0.49 0.415 0.473 0.56 0.388 0.415 0.439 0.51 0.506 0.575 0.449 0.447 0.351Q16 0.446 0.586 0.552 0.583 0.414 0.467 0.538 0.409 0.539 0.39 0.367 0.415 0.532 0.434 0.495 0.455 0.412 0.313Q17 0.47 0.543 0.475 0.594 0.436 0.371 0.567 0.401 0.599 0.456 0.376 0.38 0.416 0.425 0.506 0.434 0.384 0.395Q18 0.533 0.563 0.596 0.604 0.425 0.481 0.56 0.526 0.562 0.41 0.37 0.5 0.519 0.5 0.59 0.493 0.347 0.192Q19 0.45 0.534 0.589 0.571 0.361 0.61 0.562 0.325 0.497 0.408 0.245 0.604 0.584 0.535 0.504 0.367 0.306 0.221Q20 0.533 0.558 0.535 0.581 0.547 0.505 0.625 0.404 0.599 0.389 0.295 0.508 0.401 0.437 0.512 0.513 0.419 0.352Q21 0.554 0.656 0.545 0.518 0.505 0.57 0.584 0.362 0.614 0.437 0.392 0.434 0.485 0.431 0.473 0.477 0.535 0.47Q22 0.533 0.558 0.584 0.501 0.407 0.538 0.436 0.438 0.482 0.277 0.388 0.581 0.473 0.466 0.602 0.441 0.309 0.145Q23 0.523 0.576 0.561 0.519 0.418 0.515 0.544 0.585 0.53 0.443 0.402 0.346 0.428 0.601 0.593 0.511 0.614 0.366Q24 0.524 0.677 0.626 0.583 0.369 0.561 0.516 0.582 0.526 0.494 0.46 0.431 0.582 0.605 0.56 0.487 0.525 0.165Q25 0.577 0.763 0.7 0.642 0.41 0.668 0.609 0.588 0.648 0.621 0.539 0.481 0.541 0.661 0.588 0.566 0.663 0.345Q26 0.542 0.636 0.621 0.648 0.461 0.641 0.596 0.58 0.617 0.495 0.436 0.512 0.586 0.591 0.645 0.609 0.466 0.205Q27A 0.471 0.549 0.545 0.497 0.499 0.561 0.58 0.444 0.56 0.396 0.339 0.284 0.494 0.418 0.475 0.503 0.603 0.405Q27B 0.416 0.454 0.423 0.479 0.357 0.538 0.398 0.26 0.373 0.239 0.392 0.27 0.382 0.328 0.321 0.388 0.241 0.097Q28 0.511 0.679 0.743 0.634 0.443 0.76 0.7 0.599 0.635 0.442 0.455 0.572 0.679 0.639 0.674 0.701 0.487 0.182Q29 0.5 0.601 0.642 0.65 0.62 0.592 0.649 0.559 0.645 0.415 0.378 0.489 0.586 0.579 0.621 0.582 0.482 0.17Q30 0.524 0.567 0.669 0.615 0.546 0.647 0.587 0.408 0.491 0.374 0.36 0.428 0.532 0.477 0.49 0.627 0.446 0.23Q31A 0.504 0.556 0.529 0.514 0.593 0.397 0.49 0.524 0.522 0.206 0.49 0.377 0.254 0.364 0.338 0.652 0.399 0.388Q31B 0.482 0.584 0.604 0.637 0.462 0.593 0.653 0.568 0.644 0.407 0.409 0.474 0.474 0.536 0.516 0.591 0.428 0.17Q31C 0.564 0.577 0.65 0.626 0.521 0.572 0.564 0.627 0.63 0.414 0.413 0.545 0.46 0.512 0.556 0.642 0.391 0.178Q31D 0.536 0.574 0.549 0.599 0.501 0.501 0.546 0.447 0.605 0.381 0.467 0.545 0.404 0.487 0.467 0.557 0.342 0.211Q31E 0.451 0.584 0.588 0.609 0.465 0.471 0.527 0.457 0.581 0.434 0.432 0.513 0.43 0.496 0.418 0.552 0.389 0.273Q31F 0.51 0.608 0.642 0.67 0.549 0.565 0.633 0.459 0.569 0.422 0.337 0.521 0.471 0.523 0.484 0.585 0.386 0.226Q31G 0.429 0.579 0.534 0.568 0.532 0.468 0.614 0.538 0.642 0.397 0.458 0.428 0.436 0.407 0.421 0.628 0.397 0.244Q32A 0.45 0.531 0.473 0.475 0.484 0.344 0.465 0.339 0.5 0.402 0.307 0.327 0.482 0.418 0.397 0.386 0.271 0.222Q32B 0.483 0.618 0.595 0.513 0.528 0.518 0.569 0.429 0.579 0.403 0.35 0.32 0.531 0.497 0.446 0.421 0.63 0.378Q32C 0.485 0.607 0.609 0.493 0.613 0.481 0.606 0.364 0.418 0.276 0.174 0.373 0.383 0.347 0.356 0.463 0.545 0.271Q32D 0.413 0.565 0.612 0.575 0.507 0.563 0.636 0.571 0.53 0.364 0.242 0.423 0.606 0.578 0.547 0.522 0.541 0.238Q33 0.192 0.396 0.445 0.446 0.436 0.405 0.587 0.284 0.595 0.294 0.279 0.231 0.297 0.241 0.177 0.45 0.418 0.327Q34 0.473 0.574 0.624 0.55 0.435 0.641 0.655 0.388 0.549 0.375 0.363 0.425 0.47 0.52 0.436 0.556 0.425 0.123Q35 0.42 0.523 0.507 0.5 0.479 0.58 0.527 0.4 0.552 0.304 0.303 0.332 0.507 0.492 0.404 0.446 0.547 0.407Q36 0.42 0.432 0.436 0.527 0.55 0.565 0.553 0.385 0.538 0.182 0.378 0.35 0.437 0.439 0.404 0.471 0.497 0.498Q37A 0.507 0.575 0.474 0.503 0.576 0.564 0.499 0.299 0.398 0.255 0.461 0.42 0.334 0.337 0.349 0.581 0.571 0.463Q37B 0.491 0.593 0.526 0.566 0.545 0.547 0.576 0.311 0.408 0.245 0.144 0.439 0.42 0.411 0.459 0.485 0.532 0.225Q38 0.397 0.501 0.384 0.513 0.652 0.403 0.498 0.434 0.523 0.239 0.457 0.317 0.345 0.305 0.302 0.531 0.574 0.656Q39 0.453 0.551 0.413 0.561 0.653 0.389 0.481 0.352 0.562 0.339 0.465 0.447 0.368 0.323 0.365 0.63 0.422 0.511Q40 0.413 0.427 0.243 0.367 0.503 0.334 0.292 0.277 0.425 0.173 0.528 0.224 0.264 0.201 0.188 0.413 0.461 0.576Q41 0.474 0.525 0.455 0.498 0.693 0.528 0.492 0.355 0.434 0.244 0.455 0.319 0.357 0.367 0.256 0.577 0.572 0.562Q42 0.517 0.664 0.493 0.561 0.571 0.525 0.586 0.446 0.558 0.412 0.456 0.45 0.357 0.44 0.403 0.576 0.497 0.391Q43A 0.547 0.567 0.521 0.521 0.665 0.569 0.544 0.4 0.462 0.324 0.435 0.375 0.324 0.428 0.352 0.66 0.491 0.493Q43B 0.493 0.604 0.574 0.526 0.594 0.627 0.457 0.369 0.458 0.383 0.479 0.391 0.487 0.442 0.457 0.618 0.496 0.5Q43C 0.314 0.326 0.32 0.4 0.495 0.303 0.401 0.178 0.367 0.188 0.348 0.325 0.255 0.086 0.157 0.375 0.171 0.409Q43D 0.473 0.529 0.532 0.606 0.657 0.528 0.504 0.359 0.509 0.299 0.569 0.471 0.334 0.306 0.329 0.593 0.273 0.548Q43E 0.537 0.572 0.524 0.623 0.665 0.493 0.464 0.397 0.526 0.31 0.652 0.53 0.376 0.367 0.421 0.583 0.311 0.581Q43F 0.489 0.551 0.477 0.609 0.574 0.51 0.524 0.424 0.516 0.21 0.474 0.494 0.421 0.381 0.384 0.425 0.318 0.343Q43G 0.517 0.557 0.502 0.648 0.676 0.54 0.615 0.426 0.503 0.244 0.332 0.495 0.387 0.384 0.37 0.463 0.458 0.429Q43H 0.314 0.392 0.358 0.459 0.398 0.32 0.399 0.384 0.496 0.227 0.28 0.278 0.31 0.235 0.262 0.337 0.405 0.354Q44 0.602 0.554 0.501 0.559 0.706 0.501 0.539 0.499 0.528 0.302 0.45 0.45 0.366 0.38 0.371 0.68 0.501 0.641Q45 0.593 0.583 0.541 0.504 0.604 0.546 0.42 0.42 0.444 0.317 0.413 0.362 0.412 0.464 0.417 0.61 0.522 0.492Q46A 0.445 0.416 0.402 0.495 0.495 0.491 0.533 0.332 0.464 0.261 0.262 0.25 0.414 0.327 0.319 0.455 0.472 0.351Q46B 0.47 0.525 0.555 0.545 0.491 0.532 0.577 0.416 0.505 0.356 0.329 0.336 0.487 0.385 0.409 0.506 0.43 0.304Q46C 0.513 0.558 0.505 0.563 0.565 0.508 0.545 0.496 0.566 0.29 0.41 0.415 0.491 0.449 0.485 0.467 0.503 0.405Q47A 0.479 0.503 0.391 0.483 0.561 0.42 0.474 0.347 0.459 0.181 0.202 0.355 0.49 0.343 0.401 0.451 0.49 0.453Q47B 0.323 0.43 0.415 0.561 0.505 0.456 0.39 0.34 0.327 0.144 0.238 0.339 0.468 0.374 0.396 0.441 0.368 0.315Q48 0.396 0.454 0.427 0.486 0.556 0.486 0.494 0.402 0.483 0.202 0.24 0.294 0.545 0.399 0.454 0.46 0.443 0.352Q49 0.466 0.563 0.538 0.495 0.35 0.339 0.282 0.426 0.388 0.297 0.256 0.578 0.572 0.635 0.683 0.381 0.297 0.089Q50 0.469 0.461 0.365 0.464 0.523 0.384 0.465 0.39 0.517 0.248 0.311 0.437 0.463 0.407 0.47 0.42 0.393 0.349Q51 0.368 0.512 0.402 0.526 0.467 0.384 0.599 0.261 0.533 0.417 0.256 0.374 0.394 0.345 0.372 0.449 0.408 0.275Q52 0.458 0.498 0.472 0.47 0.549 0.398 0.359 0.407 0.385 0.373 0.56 0.511 0.447 0.366 0.463 0.637 0.323 0.4Q53 0.366 0.297 0.356 0.268 0.254 0.34 0.229 0.462 0.431 0.264 0.2 0.195 0.515 0.344 0.432 0.25 0.456 0.265Q54 0.378 0.514 0.504 0.556 0.452 0.566 0.506 0.55 0.557 0.381 0.278 0.438 0.615 0.61 0.633 0.483 0.491 0.232Q55 0.231 0.229 0.197 0.222 0.271 0.259 0.281 0.248 0.337 0.237 0.191 0.055 0.332 0.213 0.266 0.272 0.541 0.415Q56 0.262 0.285 0.328 0.247 0.158 0.336 0.294 0.282 0.344 0.319 0.235 0.174 0.441 0.343 0.418 0.312 0.465 0.249Q57 0.283 0.302 0.352 0.358 0.252 0.445 0.361 0.278 0.32 0.16 0.202 0.21 0.471 0.364 0.45 0.31 0.409 0.194Q58 0.286 0.278 0.315 0.355 0.275 0.405 0.248 0.439 0.452 0.11 0.38 0.189 0.436 0.382 0.404 0.32 0.446 0.283Q59 0.338 0.327 0.335 0.309 0.296 0.375 0.231 0.282 0.385 0.146 0.296 0.273 0.501 0.332 0.361 0.234 0.371 0.255

108

Q14C Q14D Q14E Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27A Q27B Q28Q14C 1Q14D 0.647 1Q14E 0.583 0.538 1Q15 0.49 0.444 0.592 1Q16 0.461 0.411 0.564 0.755 1Q17 0.483 0.302 0.519 0.715 0.762 1Q18 0.54 0.43 0.548 0.567 0.46 0.62 1Q19 0.456 0.41 0.549 0.538 0.545 0.524 0.607 1Q20 0.57 0.354 0.507 0.496 0.416 0.63 0.642 0.55 1Q21 0.634 0.592 0.598 0.705 0.676 0.622 0.519 0.584 0.601 1Q22 0.407 0.476 0.504 0.571 0.503 0.517 0.572 0.57 0.56 0.616 1Q23 0.531 0.349 0.611 0.546 0.504 0.592 0.509 0.454 0.505 0.517 0.451 1Q24 0.555 0.41 0.779 0.56 0.481 0.505 0.518 0.387 0.437 0.506 0.431 0.646 1Q25 0.74 0.557 0.74 0.646 0.617 0.643 0.577 0.517 0.572 0.665 0.484 0.726 0.778 1Q26 0.569 0.477 0.602 0.566 0.58 0.555 0.677 0.445 0.522 0.602 0.553 0.54 0.618 0.703 1Q27A 0.491 0.443 0.646 0.593 0.655 0.585 0.485 0.393 0.501 0.721 0.522 0.61 0.506 0.631 0.642 1Q27B 0.406 0.531 0.504 0.468 0.452 0.376 0.362 0.328 0.303 0.512 0.346 0.341 0.384 0.498 0.458 0.48 1Q28 0.561 0.49 0.65 0.429 0.479 0.385 0.555 0.502 0.486 0.463 0.502 0.549 0.634 0.676 0.712 0.607 0.412 1Q29 0.454 0.436 0.553 0.418 0.325 0.4 0.584 0.417 0.57 0.466 0.512 0.487 0.531 0.566 0.662 0.603 0.377 0.754Q30 0.428 0.51 0.64 0.609 0.543 0.44 0.442 0.577 0.44 0.609 0.489 0.527 0.45 0.537 0.476 0.659 0.597 0.67Q31A 0.545 0.481 0.347 0.346 0.344 0.458 0.393 0.244 0.538 0.43 0.416 0.547 0.41 0.563 0.396 0.423 0.255 0.511Q31B 0.54 0.39 0.473 0.412 0.506 0.516 0.536 0.436 0.564 0.499 0.428 0.584 0.535 0.657 0.601 0.538 0.408 0.71Q31C 0.624 0.442 0.496 0.501 0.479 0.446 0.558 0.436 0.619 0.567 0.524 0.463 0.475 0.625 0.656 0.551 0.392 0.703Q31D 0.593 0.409 0.439 0.475 0.477 0.535 0.48 0.391 0.634 0.506 0.4 0.418 0.459 0.637 0.55 0.438 0.383 0.601Q31E 0.576 0.365 0.481 0.405 0.413 0.516 0.4 0.447 0.584 0.379 0.332 0.453 0.487 0.659 0.427 0.39 0.272 0.651Q31F 0.59 0.437 0.49 0.481 0.488 0.5 0.534 0.452 0.611 0.575 0.377 0.473 0.496 0.665 0.648 0.544 0.43 0.682Q31G 0.616 0.44 0.412 0.397 0.487 0.492 0.463 0.364 0.623 0.524 0.412 0.428 0.446 0.616 0.633 0.536 0.313 0.722Q32A 0.51 0.35 0.556 0.44 0.428 0.475 0.466 0.414 0.655 0.586 0.349 0.441 0.432 0.528 0.447 0.528 0.47 0.378Q32B 0.538 0.44 0.674 0.557 0.564 0.592 0.518 0.456 0.526 0.696 0.512 0.63 0.595 0.647 0.649 0.796 0.438 0.601Q32C 0.52 0.361 0.479 0.32 0.426 0.352 0.392 0.336 0.615 0.607 0.433 0.47 0.415 0.492 0.483 0.641 0.31 0.614Q32D 0.505 0.383 0.576 0.406 0.452 0.419 0.477 0.501 0.49 0.474 0.355 0.571 0.546 0.552 0.622 0.595 0.238 0.69Q33 0.531 0.376 0.48 0.293 0.379 0.501 0.446 0.405 0.623 0.555 0.257 0.38 0.331 0.511 0.45 0.625 0.398 0.555Q34 0.578 0.431 0.525 0.442 0.451 0.526 0.458 0.488 0.584 0.546 0.451 0.535 0.532 0.679 0.506 0.545 0.477 0.695Q35 0.512 0.537 0.575 0.592 0.518 0.467 0.392 0.49 0.419 0.715 0.35 0.532 0.458 0.587 0.493 0.717 0.54 0.549Q36 0.485 0.497 0.549 0.577 0.443 0.574 0.483 0.468 0.581 0.576 0.368 0.516 0.389 0.504 0.476 0.661 0.511 0.511Q37A 0.44 0.614 0.47 0.448 0.406 0.352 0.251 0.226 0.42 0.623 0.408 0.265 0.464 0.503 0.444 0.464 0.347 0.333Q37B 0.371 0.406 0.407 0.471 0.468 0.399 0.358 0.364 0.544 0.581 0.454 0.398 0.401 0.416 0.443 0.538 0.41 0.45Q38 0.456 0.61 0.51 0.529 0.555 0.58 0.324 0.325 0.507 0.656 0.417 0.477 0.389 0.457 0.405 0.573 0.312 0.31Q39 0.479 0.527 0.472 0.503 0.5 0.539 0.407 0.326 0.626 0.63 0.439 0.391 0.406 0.484 0.517 0.534 0.249 0.407Q40 0.416 0.558 0.47 0.377 0.436 0.376 0.258 0.213 0.369 0.492 0.262 0.368 0.403 0.397 0.321 0.537 0.376 0.249Q41 0.429 0.547 0.314 0.377 0.331 0.287 0.243 0.242 0.344 0.417 0.223 0.392 0.32 0.467 0.328 0.538 0.343 0.477Q42 0.735 0.48 0.453 0.392 0.374 0.476 0.382 0.383 0.585 0.456 0.273 0.451 0.437 0.65 0.45 0.41 0.43 0.513Q43A 0.476 0.497 0.315 0.415 0.373 0.398 0.309 0.364 0.42 0.409 0.214 0.487 0.355 0.534 0.338 0.407 0.291 0.459Q43B 0.435 0.612 0.559 0.571 0.509 0.344 0.366 0.421 0.351 0.621 0.363 0.446 0.415 0.518 0.456 0.644 0.424 0.585Q43C 0.226 0.329 0.235 0.461 0.46 0.414 0.306 0.341 0.338 0.485 0.263 0.089 0.193 0.232 0.225 0.394 0.152 0.33Q43D 0.45 0.615 0.446 0.549 0.511 0.525 0.418 0.455 0.503 0.58 0.378 0.323 0.293 0.488 0.406 0.533 0.437 0.494Q43E 0.436 0.613 0.467 0.587 0.542 0.567 0.415 0.445 0.508 0.566 0.485 0.427 0.324 0.483 0.391 0.516 0.392 0.457Q43F 0.511 0.613 0.504 0.447 0.505 0.482 0.462 0.457 0.578 0.555 0.452 0.367 0.361 0.493 0.515 0.552 0.556 0.521Q43G 0.521 0.518 0.433 0.431 0.429 0.472 0.463 0.474 0.603 0.621 0.403 0.399 0.36 0.504 0.502 0.633 0.374 0.536Q43H 0.33 0.25 0.39 0.387 0.493 0.53 0.294 0.344 0.445 0.413 0.358 0.41 0.403 0.42 0.25 0.536 0.3 0.462Q44 0.573 0.504 0.406 0.436 0.455 0.478 0.367 0.305 0.524 0.544 0.369 0.491 0.437 0.547 0.506 0.574 0.266 0.514Q45 0.464 0.51 0.415 0.443 0.293 0.319 0.295 0.269 0.34 0.509 0.345 0.46 0.467 0.536 0.416 0.402 0.393 0.443Q46A 0.415 0.447 0.408 0.535 0.591 0.521 0.359 0.468 0.461 0.668 0.341 0.413 0.276 0.472 0.43 0.666 0.501 0.444Q46B 0.504 0.499 0.568 0.571 0.685 0.532 0.436 0.509 0.435 0.746 0.428 0.457 0.381 0.524 0.554 0.742 0.545 0.549Q46C 0.585 0.514 0.592 0.59 0.66 0.588 0.461 0.493 0.535 0.765 0.542 0.606 0.424 0.572 0.591 0.791 0.503 0.526Q47A 0.444 0.407 0.522 0.576 0.537 0.519 0.425 0.377 0.596 0.616 0.391 0.421 0.415 0.437 0.441 0.654 0.408 0.44Q47B 0.306 0.407 0.524 0.538 0.452 0.397 0.322 0.387 0.414 0.511 0.358 0.389 0.425 0.356 0.456 0.492 0.489 0.475Q48 0.407 0.387 0.557 0.519 0.465 0.428 0.369 0.421 0.584 0.589 0.389 0.443 0.407 0.374 0.39 0.631 0.434 0.511Q49 0.341 0.235 0.512 0.545 0.364 0.421 0.503 0.411 0.39 0.406 0.555 0.518 0.552 0.461 0.472 0.316 0.287 0.463Q50 0.459 0.32 0.509 0.525 0.487 0.541 0.408 0.428 0.667 0.543 0.441 0.491 0.381 0.377 0.36 0.577 0.303 0.396Q51 0.43 0.216 0.394 0.434 0.589 0.69 0.503 0.433 0.661 0.554 0.39 0.471 0.424 0.539 0.52 0.555 0.244 0.41Q52 0.369 0.548 0.498 0.416 0.446 0.311 0.337 0.359 0.33 0.415 0.496 0.422 0.323 0.358 0.342 0.431 0.178 0.453Q53 0.349 0.3 0.49 0.584 0.523 0.337 0.361 0.442 0.304 0.506 0.468 0.483 0.363 0.371 0.347 0.577 0.287 0.443Q54 0.471 0.44 0.569 0.534 0.468 0.445 0.46 0.525 0.596 0.547 0.512 0.63 0.463 0.542 0.649 0.618 0.343 0.601Q55 0.426 0.18 0.353 0.265 0.379 0.303 0.23 0.218 0.415 0.394 0.258 0.39 0.284 0.319 0.316 0.543 0.153 0.364Q56 0.339 0.241 0.479 0.348 0.433 0.297 0.329 0.394 0.399 0.452 0.425 0.535 0.374 0.384 0.39 0.572 0.174 0.5Q57 0.299 0.289 0.49 0.407 0.468 0.284 0.316 0.409 0.393 0.435 0.439 0.492 0.363 0.358 0.366 0.521 0.406 0.481Q58 0.406 0.468 0.569 0.555 0.481 0.328 0.384 0.333 0.372 0.555 0.485 0.474 0.41 0.455 0.544 0.689 0.432 0.439Q59 0.349 0.406 0.519 0.451 0.479 0.289 0.219 0.364 0.338 0.499 0.483 0.236 0.336 0.28 0.33 0.555 0.394 0.39

Q29 Q30 Q31A Q31B Q31C Q31D Q31E Q31F Q31G Q32A Q32B Q32C Q32D Q33 Q34 Q35 Q36 Q37AQ29 1Q30 0.622 1Q31A 0.449 0.356 1Q31B 0.644 0.528 0.694 1Q31C 0.681 0.514 0.64 0.826 1Q31D 0.548 0.426 0.681 0.8 0.844 1Q31E 0.544 0.482 0.689 0.747 0.709 0.856 1Q31F 0.608 0.497 0.664 0.841 0.844 0.858 0.742 1Q31G 0.665 0.501 0.671 0.772 0.8 0.817 0.806 0.79 1Q32A 0.488 0.506 0.347 0.48 0.471 0.482 0.488 0.551 0.52 1Q32B 0.65 0.644 0.498 0.633 0.531 0.538 0.546 0.603 0.579 0.599 1Q32C 0.568 0.6 0.557 0.575 0.57 0.538 0.495 0.614 0.652 0.527 0.694 1Q32D 0.59 0.527 0.429 0.593 0.594 0.542 0.507 0.648 0.593 0.455 0.617 0.634 1Q33 0.517 0.521 0.516 0.572 0.57 0.637 0.65 0.661 0.673 0.543 0.643 0.637 0.56 1Q34 0.529 0.613 0.685 0.8 0.697 0.784 0.758 0.794 0.7 0.479 0.671 0.626 0.605 0.709 1Q35 0.465 0.708 0.408 0.542 0.525 0.545 0.493 0.618 0.498 0.511 0.777 0.576 0.655 0.654 0.679 1Q36 0.532 0.673 0.408 0.477 0.488 0.57 0.536 0.544 0.535 0.511 0.675 0.506 0.549 0.712 0.616 0.807 1Q37A 0.4 0.478 0.459 0.356 0.35 0.382 0.298 0.373 0.36 0.326 0.486 0.466 0.346 0.307 0.395 0.451 0.394 1Q37B 0.497 0.609 0.403 0.491 0.443 0.421 0.329 0.468 0.457 0.372 0.575 0.665 0.488 0.358 0.491 0.566 0.488 0.665Q38 0.357 0.513 0.542 0.391 0.348 0.4 0.39 0.382 0.429 0.422 0.618 0.474 0.417 0.459 0.41 0.618 0.618 0.702Q39 0.447 0.422 0.563 0.445 0.546 0.562 0.495 0.527 0.619 0.461 0.473 0.458 0.403 0.522 0.43 0.474 0.551 0.592Q40 0.303 0.406 0.402 0.288 0.243 0.266 0.241 0.252 0.258 0.436 0.471 0.357 0.222 0.377 0.247 0.416 0.441 0.573

109

Q29 Q30 Q31A Q31B Q31C Q31D Q31E Q31F Q31G Q32A Q32B Q32C Q32D Q33 Q34 Q35 Q36 Q37AQ41 0.48 0.523 0.568 0.39 0.384 0.458 0.504 0.467 0.5 0.331 0.514 0.543 0.387 0.5 0.467 0.585 0.619 0.505Q42 0.532 0.506 0.525 0.568 0.586 0.669 0.735 0.609 0.705 0.634 0.481 0.492 0.477 0.563 0.614 0.477 0.57 0.414Q43A 0.413 0.452 0.637 0.441 0.473 0.521 0.534 0.491 0.454 0.295 0.35 0.425 0.446 0.452 0.525 0.49 0.55 0.484Q43B 0.466 0.612 0.432 0.413 0.514 0.405 0.369 0.486 0.395 0.394 0.521 0.477 0.48 0.415 0.457 0.668 0.568 0.497Q43C 0.246 0.41 0.266 0.204 0.272 0.301 0.333 0.269 0.388 0.242 0.324 0.391 0.26 0.427 0.287 0.422 0.459 0.358Q43D 0.413 0.545 0.518 0.425 0.539 0.528 0.533 0.51 0.53 0.428 0.434 0.429 0.359 0.557 0.482 0.569 0.674 0.439Q43E 0.407 0.556 0.543 0.414 0.455 0.494 0.535 0.413 0.492 0.491 0.479 0.426 0.351 0.434 0.415 0.52 0.633 0.484Q43F 0.502 0.591 0.418 0.518 0.536 0.599 0.54 0.53 0.603 0.576 0.58 0.601 0.513 0.582 0.544 0.627 0.713 0.38Q43G 0.597 0.601 0.494 0.568 0.61 0.614 0.603 0.615 0.67 0.501 0.633 0.72 0.581 0.66 0.583 0.676 0.738 0.427Q43H 0.379 0.448 0.432 0.45 0.427 0.442 0.514 0.41 0.472 0.408 0.536 0.541 0.434 0.554 0.474 0.501 0.513 0.202Q44 0.421 0.477 0.73 0.536 0.54 0.526 0.574 0.59 0.608 0.449 0.558 0.605 0.52 0.518 0.527 0.562 0.562 0.569Q45 0.396 0.424 0.564 0.397 0.429 0.388 0.43 0.437 0.372 0.331 0.471 0.397 0.356 0.297 0.436 0.547 0.437 0.544Q46A 0.429 0.706 0.389 0.516 0.535 0.538 0.46 0.529 0.576 0.419 0.59 0.578 0.567 0.582 0.587 0.788 0.705 0.46Q46B 0.483 0.726 0.34 0.567 0.644 0.528 0.412 0.597 0.561 0.519 0.62 0.589 0.615 0.58 0.567 0.76 0.636 0.412Q46C 0.518 0.67 0.447 0.554 0.61 0.524 0.424 0.577 0.592 0.647 0.693 0.671 0.6 0.572 0.544 0.735 0.695 0.436Q47A 0.401 0.566 0.367 0.363 0.398 0.445 0.437 0.496 0.529 0.615 0.563 0.604 0.543 0.573 0.445 0.648 0.741 0.423Q47B 0.421 0.586 0.252 0.405 0.435 0.445 0.415 0.496 0.462 0.402 0.543 0.481 0.586 0.445 0.453 0.693 0.693 0.333Q48 0.475 0.672 0.283 0.372 0.391 0.387 0.383 0.412 0.47 0.572 0.566 0.627 0.612 0.533 0.467 0.707 0.721 0.388Q49 0.468 0.409 0.258 0.316 0.38 0.34 0.336 0.353 0.275 0.347 0.422 0.335 0.468 0.161 0.309 0.379 0.286 0.22Q50 0.379 0.57 0.406 0.409 0.407 0.482 0.459 0.388 0.472 0.555 0.517 0.579 0.496 0.512 0.463 0.567 0.675 0.384Q51 0.376 0.311 0.486 0.524 0.463 0.617 0.548 0.555 0.596 0.418 0.488 0.522 0.547 0.65 0.556 0.411 0.496 0.328Q52 0.328 0.504 0.484 0.316 0.333 0.255 0.319 0.279 0.315 0.312 0.327 0.35 0.368 0.182 0.284 0.28 0.228 0.471Q53 0.38 0.55 0.143 0.234 0.301 0.138 0.217 0.202 0.276 0.374 0.492 0.373 0.467 0.262 0.237 0.514 0.415 0.158Q54 0.585 0.549 0.375 0.489 0.511 0.399 0.408 0.485 0.52 0.492 0.573 0.497 0.719 0.433 0.496 0.593 0.572 0.324Q55 0.362 0.373 0.253 0.291 0.275 0.219 0.27 0.257 0.383 0.34 0.489 0.513 0.365 0.39 0.276 0.337 0.352 0.288Q56 0.393 0.441 0.236 0.352 0.336 0.226 0.276 0.298 0.372 0.296 0.5 0.42 0.436 0.339 0.323 0.366 0.294 0.186Q57 0.442 0.514 0.215 0.314 0.269 0.128 0.152 0.266 0.247 0.347 0.451 0.412 0.4 0.269 0.315 0.379 0.317 0.258Q58 0.471 0.588 0.242 0.353 0.4 0.287 0.226 0.325 0.394 0.417 0.596 0.397 0.464 0.389 0.336 0.61 0.595 0.388Q59 0.355 0.566 0.138 0.285 0.29 0.292 0.308 0.241 0.3 0.406 0.608 0.419 0.353 0.343 0.336 0.564 0.516 0.403

Q37B Q38 Q39 Q40 Q41 Q42 Q43A Q43B Q43C Q43D Q43E Q43F Q43G Q43H Q44 Q45 Q46A Q46BQ37B 1Q38 0.566 1Q39 0.43 0.771 1Q40 0.371 0.712 0.635 1Q41 0.445 0.583 0.511 0.578 1Q42 0.417 0.396 0.457 0.345 0.554 1Q43A 0.351 0.501 0.491 0.484 0.795 0.583 1Q43B 0.429 0.558 0.53 0.596 0.686 0.405 0.717 1Q43C 0.225 0.477 0.521 0.395 0.514 0.261 0.451 0.581 1Q43D 0.308 0.555 0.615 0.534 0.636 0.524 0.692 0.776 0.752 1Q43E 0.366 0.625 0.617 0.596 0.612 0.515 0.635 0.739 0.708 0.913 1Q43F 0.475 0.544 0.53 0.497 0.478 0.586 0.407 0.494 0.481 0.708 0.705 1Q43G 0.561 0.556 0.554 0.454 0.602 0.618 0.513 0.546 0.557 0.722 0.668 0.854 1Q43H 0.371 0.469 0.355 0.422 0.497 0.411 0.335 0.441 0.531 0.54 0.574 0.54 0.573 1Q44 0.462 0.65 0.659 0.579 0.721 0.581 0.716 0.625 0.525 0.703 0.703 0.562 0.674 0.471 1Q45 0.432 0.56 0.488 0.476 0.682 0.479 0.689 0.683 0.363 0.539 0.546 0.307 0.416 0.3 0.732 1Q46A 0.626 0.648 0.56 0.434 0.54 0.443 0.486 0.563 0.501 0.556 0.511 0.662 0.716 0.456 0.549 0.455 1Q46B 0.535 0.58 0.554 0.457 0.435 0.447 0.407 0.637 0.486 0.579 0.52 0.655 0.666 0.465 0.514 0.432 0.875 1Q46C 0.553 0.653 0.62 0.562 0.479 0.512 0.411 0.624 0.437 0.59 0.638 0.701 0.722 0.577 0.58 0.429 0.766 0.858Q47A 0.528 0.65 0.682 0.502 0.505 0.49 0.385 0.481 0.476 0.518 0.529 0.654 0.657 0.497 0.59 0.403 0.662 0.621Q47B 0.497 0.522 0.443 0.293 0.432 0.404 0.299 0.502 0.415 0.488 0.468 0.61 0.611 0.438 0.466 0.426 0.657 0.628Q48 0.585 0.634 0.53 0.434 0.492 0.434 0.394 0.583 0.485 0.49 0.511 0.611 0.625 0.493 0.515 0.458 0.729 0.673Q49 0.324 0.266 0.304 0.161 0.19 0.272 0.172 0.28 0.231 0.216 0.325 0.305 0.278 0.327 0.26 0.349 0.238 0.345Q50 0.587 0.641 0.603 0.491 0.417 0.465 0.398 0.434 0.443 0.442 0.554 0.604 0.596 0.489 0.53 0.344 0.638 0.601Q51 0.41 0.466 0.611 0.318 0.33 0.447 0.385 0.245 0.402 0.391 0.395 0.476 0.55 0.457 0.501 0.237 0.524 0.491Q52 0.271 0.552 0.582 0.524 0.394 0.265 0.397 0.556 0.396 0.448 0.598 0.3 0.268 0.267 0.545 0.388 0.301 0.386Q53 0.318 0.413 0.299 0.336 0.286 0.156 0.192 0.504 0.411 0.293 0.406 0.332 0.327 0.464 0.323 0.328 0.508 0.558Q54 0.55 0.511 0.494 0.306 0.314 0.408 0.369 0.503 0.204 0.359 0.419 0.54 0.535 0.287 0.427 0.361 0.568 0.554Q55 0.387 0.439 0.375 0.421 0.355 0.229 0.232 0.372 0.298 0.209 0.287 0.267 0.346 0.392 0.465 0.304 0.476 0.422Q56 0.383 0.369 0.335 0.368 0.231 0.113 0.139 0.435 0.233 0.185 0.291 0.22 0.277 0.379 0.323 0.261 0.435 0.475Q57 0.475 0.342 0.212 0.371 0.237 0.136 0.176 0.446 0.205 0.21 0.306 0.283 0.264 0.349 0.3 0.287 0.425 0.445Q58 0.446 0.52 0.464 0.509 0.319 0.213 0.195 0.503 0.271 0.359 0.434 0.54 0.478 0.269 0.412 0.313 0.629 0.627Q59 0.539 0.517 0.327 0.445 0.32 0.223 0.12 0.424 0.323 0.302 0.424 0.494 0.425 0.423 0.363 0.307 0.53 0.527

Q46C Q47A Q47B Q48 Q49 Q50 Q51 Q52 Q53 Q54 Q55 Q56 Q57 Q58 Q59Q46C 1Q47A 0.713 1Q47B 0.593 0.701 1Q48 0.707 0.813 0.793 1Q49 0.42 0.386 0.537 0.43 1Q50 0.705 0.766 0.552 0.804 0.411 1Q51 0.516 0.615 0.419 0.494 0.36 0.632 1Q52 0.454 0.405 0.314 0.409 0.416 0.492 0.332 1Q53 0.588 0.566 0.505 0.652 0.481 0.524 0.288 0.478 1Q54 0.65 0.605 0.609 0.694 0.497 0.64 0.488 0.438 0.619 1Q55 0.498 0.431 0.355 0.594 0.149 0.515 0.423 0.346 0.591 0.44 1Q56 0.5 0.358 0.371 0.527 0.324 0.471 0.395 0.463 0.734 0.557 0.806 1Q57 0.496 0.371 0.47 0.583 0.379 0.433 0.294 0.433 0.72 0.613 0.717 0.825 1Q58 0.694 0.557 0.554 0.639 0.32 0.564 0.302 0.378 0.691 0.69 0.614 0.665 0.685 1Q59 0.538 0.52 0.547 0.675 0.286 0.626 0.291 0.376 0.663 0.48 0.642 0.643 0.626 0.705 1

110

Appendix E: EQS output � English sample initial single-group model run

The EQS code is printed with permission of the EQS copyright holder Peter M. Bentler. EQS, A STRUCTURAL EQUATION PROGRAM MULTIVARIATE SOFTWARE, INC. COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) 1985 - 1998. PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model created by EQS 5.7b -- 5F389B34.EDS 3 /SPECIFICATIONS 4 DATA='C:\THESIS\EN_IMP~1\T_EIMP2.ESS'; 5 VARIABLES= 89; CASES= 459; 6 METHODS=ML,ROBUST; 7 MATRIX=RAW; 8 /LABELS 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q12B; 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V20=Q14C; 13 V21=Q14D; V22=Q14E; V23=Q15; V24=Q16; V25=Q17; 14 V26=Q18; V27=Q19; V28=Q20; V29=Q21; V30=Q22; 15 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 17 V41=Q31B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F; 18 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50=Q32D; 19 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 20 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V60=Q41; 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43D; 22 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 24 V76=Q47B; V77=Q48; V78=Q49; V79=Q50; V80=Q51; 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q58; V88=Q59; V89=Q12AB_CO; 27 /EQUATION 28 V14 = 1.000 F4 + 1.000 E14 ; 29 V15 = 1.170*F4 + 1.000 E15 ; 30 V16 = 1.099*F4 + 1.000 E16 ; 31 V17 = 1.000 F5 + 1.000 E17 ; 32 V18 = 1.047*F5 + 1.000 E18 ; 33 V19 = 1.066*F5 + 1.000 E19 ; 34 V20 = 1.011*F5 + 1.000 E20 ; 35 V21 = 1.165*F5 + 1.000 E21 ; 36 V22 = 1.100*F5 + 1.000 E22 ; 37 V40 = 1.000 F10 + 1.000 E40 ; 38 V41 = 1.030*F10 + 1.000 E41 ; 39 V42 = .998*F10 + 1.000 E42 ; 40 V43 = 1.078*F10 + 1.000 E43 ; 41 V44 = .927*F10 + 1.000 E44 ; 42 V45 = 1.044*F10 + 1.000 E45 ; 43 V46 = 1.121*F10 + 1.000 E46 ; 44 V47 = 1.000 F11 + 1.000 E47 ; 45 V48 = 1.885*F11 + 1.000 E48 ; 46 V49 = 2.025*F11 + 1.000 E49 ; 47 V50 = 2.101*F11 + 1.000 E50 ; 48 V51 = 2.726*F11 + 1.000 E51 ; 49 V52 = 2.465*F11 + 1.000 E52 ; 50 V53 = 2.924*F11 + 1.000 E53 ; 51 V54 = 2.800*F11 + 1.000 E54 ; 52 V78 = 1.000 F17 + 1.000 E78 ;

111

TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 2 EQS/EM386 Licensee: Mikhail Koulikov 53 V79 = 1.671*F17 + 1.000 E79 ; 54 V80 = 1.805*F17 + 1.000 E80 ; 55 V81 = 1.740*F17 + 1.000 E81 ; 56 V82 = 1.000 F18 + 1.000 E82 ; 57 V83 = .887*F18 + 1.000 E83 ; 58 V84 = 1.046*F18 + 1.000 E84 ; 59 V85 = .990*F18 + 1.000 E85 ; 60 V86 = 1.059*F18 + 1.000 E86 ; 61 V87 = 1.207*F18 + 1.000 E87 ; 62 V88 = .819*F18 + 1.000 E88 ; 63 /VARIANCES 64 F4= .423* ; 65 F5= .366* ; 66 F10= .853* ; 67 F11= .091* ; 68 F17= .204* ; 69 F18= .677* ; 70 E14= .122* ; 71 E15= .028* ; 72 E16= .156* ; 73 E17= .774* ; 74 E18= .791* ; 75 E19= .618* ; 76 E20= .366* ; 77 E21= .300* ; 78 E22= .463* ; 79 E40= .531* ; 80 E41= .363* ; 81 E42= .412* ; 82 E43= .348* ; 83 E44= .379* ; 84 E45= .458* ; 85 E46= .295* ; 86 E47= .498* ; 87 E48= .373* ; 88 E49= .347* ; 89 E50= .335* ; 90 E51= .471* ; 91 E52= .461* ; 92 E53= .516* ; 93 E54= .350* ; 94 E78= .376* ; 95 E79= .316* ; 96 E80= .440* ; 97 E81= .302* ; 98 E82= .278* ; 99 E83= .466* ; 100 E84= .459* ; 101 E85= .267* ; 102 E86= .171* ; 103 E87= .294* ; 104 E88= .332* ; 105 /COVARIANCES 106 F5,F4 = .179* ; 107 F10,F4 = .137* ; 108 F10,F5 = .228* ; 109 F11,F4 = .078* ; TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 3 EQS/EM386 Licensee: Mikhail Koulikov 110 F11,F5 = .134* ; 111 F11,F10 = .138* ; 112 F17,F4 = .094* ; 113 F17,F5 = .146* ; 114 F17,F10 = .238* ; 115 F17,F11 = .080* ; 116 F18,F4 = .219* ; 117 F18,F5 = .343* ; 118 F18,F10 = .400* ; 119 F18,F11 = .198* ; 120 F18,F17 = .269* ; 121 /LMTEST 122 PROCESS=SIMULTANEOUS; 123 SET=PVV,PFV,PFF,PEE,PDD,GVV,GVF,GFV,GFF,BVF,BFF; 124 /WTEST 1 1 1

25 PVAL=0.05; 26 PRIORITY=ZERO; 27 /OUTPUT

128 parameters; 129 standard errors; 130 listing; 131 data='EQSOUT&.ETS'; 132 /END 132 RECORDS OF INPUT MODEL FILE WERE READ DATA IS READ FROM C:\THESIS\EN_IMP~1\T_EIMP2.ESS THERE ARE 89 VARIABLES AND 459 CASES IT IS A RAW DATA ESS FILE

112

TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 4 EQS/EM386 Licensee: SAMPLE STATISTICS BASED ON COMPLETE CASES

ARIABLE Q12A Q12B Q12C Q13 Q14A

IS (G2) 2.4958 2.4726 2.7548 -0.5398 -0.3705 8161 1.0676 1.0918

Q31A

07

58

7131

4

4

ATE KURTOSIS = 0.1636

= 0.3024

ALIZED MULTIVARIATE KURTOSIS:

CASE NUMBER 35 47 281 355 451 ESTIMATE 2293.4569 2328.4450 1702.9165 1850.3116 1688.7341

Mikhail Koulikov

UNIVARIATE STATISTICS --------------------- V MEAN 1.9635 2.0046 2.0463 2.9955 2.7257 SKEWNESS (G1) 1.0734 1.1564 1.2849 0.2462 0.6930 KURTOS STANDARD DEV. 0.7380 0.7791 0. VARIABLE Q14B Q14C Q14D Q14E MEAN 2.6569 2.5213 2.6796 2.6714 2.4633 SKEWNESS (G1) 0.7470 1.0043 0.7471 0.6618 0.68 KURTOSIS (G2) -0.0670 1.1043 0.4712 0.2002 -0.2444 STANDARD DEV. 1.0172 0.8604 0.8929 0.9523 1.1762 VARIABLE Q31B Q31C Q31D Q31E Q31F MEAN 2.4120 2.5485 2.4653 2.2348 2.60 SKEWNESS (G1) 0.6057 0.5265 0.6306 0.7215 0.3958 KURTOSIS (G2) -0.3215 -0.3329 -0.3384 0.0126 -0.6201 STANDARD DEV. 1.1261 1.1231 1.1571 1.0548 1.1777 VARIABLE Q31G Q32A Q32B Q32C Q32D MEAN 2.4699 2.2744 2.4521 2.4240 2.3980 SKEWNESS (G1) 0.5201 0.8855 0.8656 0.9184 0.8323 KURTOSIS (G2) -0.5117 2.0641 1.0557 1.2368 1.1149 STANDARD DEV. 1.1690 0.7673 0.8343 0.8484 0.8579 VARIABLE Q33 Q34 Q35 Q36 Q49 MEAN 2.9835 2.7671 2.9610 2.6752 1.8093 SKEWNESS (G1) 0.3758 0.4300 0.3007 0.5288 1.2328 KURTOSIS (G2) -0.5042 -0.1731 -0.6359 0.0936 2.8132 STANDARD DEV. 1.0705 1.0065 1.1372 1.0307 0.7615 VARIABLE Q50 Q51 Q52 Q53 Q54 MEAN 2.0446 2.1336 2.3489 2.4360 2.5698 SKEWNESS (G1) 1.0135 1.0844 0.7493 0.5879 0. KURTOSIS (G2) 1.0842 0.8864 0.7948 0.1410 0.2351 STANDARD DEV. 0.9405 1.0511 0.9588 0.9773 0.999 VARIABLE Q55 Q56 Q57 Q58 Q59 MEAN 2.5774 2.2873 2.3955 2.6554 2.6039 SKEWNESS (G1) 0.5796 0.7610 0.6048 0.4939 0.3295 KURTOSIS (G2) -0.1173 0.5959 0.3116 -0.3478 0.3661 STANDARD DEV. 1.0955 0.9650 0.9649 1.1314 0.886 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 391.6095 NORMALIZED ESTIMATE = 82.4289 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.3024 MEAN SCALED UNIVARI MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORM ---------------------------------------------------------------------------

113

TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 5 EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 35 VARIABLES (SELECTED FROM 89 VARIABLES) BASED ON 459 CASES. [OMITTED]

0

21 22 40 48 49 50 81 82 83

ORDS OF MEMORY.

03/16/03 PAGE : 6

BUTION THEORY)

N DESCENDING ORDER) [OMITTED]

03/16/03 PAGE : 7

BUTION THEORY)

TORED IN EQSOUT&.ETS

4 F11,F5 17,F17 F18,F4 15,E15 E16,E16 E40,E40 E41,E41 E48,E48 E49,E49 E79,E79 E80,E80 E87,E87 E88,E88 22,F5 V41,F10

V49,F11 V50,F11 V81,F17 V83,F18

L MATRIX IN THIS HE SEQUENCE LL INDEPENDENT

: [OMITTTED]

RRORS

IABLES ARIABLES

)

ON IS: 45

BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 35 DEPENDENT V'S : 14 15 16 17 18 19 20 21 22 40 DEPENDENT V'S : 41 42 43 44 45 46 47 48 49 5 DEPENDENT V'S : 51 52 53 54 78 79 80 81 82 83 DEPENDENT V'S : 84 85 86 87 88 NUMBER OF INDEPENDENT VARIABLES = 41 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 14 15 16 17 18 19 20 INDEPENDENT E'S : 41 42 43 44 45 46 47 INDEPENDENT E'S : 51 52 53 54 78 79 80 INDEPENDENT E'S : 84 85 86 87 88 NUMBER OF FREE PARAMETERS = 85 NUMBER OF FIXED NONZERO PARAMETERS = 41 3RD STAGE OF COMPUTATION REQUIRED 354818 W PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IS 0.54222E-12 TITLE: Model created by EQS 5.7b -- 5F389B34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI CASE CONTRIBUTION TO PARAMETER VARIANCES (I TITLE: Model created by EQS 5.7b -- 5F389B34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI FOLLOWING TECHNICAL INFORMATION HAS BEEN S PARAMETERS TO BE PRINTED ARE: F4,F4 F5,F4 F5,F5 F10,F4 F10,F5 F10,F10 F11,F F11,F10 F11,F11 F17,F4 F17,F5 F17,F10 F17,F11 F F18,F5 F18,F10 F18,F11 F18,F17 F18,F18 E14,E14 E E17,E17 E18,E18 E19,E19 E20,E20 E21,E21 E22,E22 E42,E42 E43,E43 E44,E44 E45,E45 E46,E46 E47,E47 E50,E50 E51,E51 E52,E52 E53,E53 E54,E54 E78,E78 E81,E81 E82,E82 E83,E83 E84,E84 E85,E85 E86,E86 V15,F4 V16,F4 V18,F5 V19,F5 V20,F5 V21,F5 V V42,F10 V43,F10 V44,F10 V45,F10 V46,F10 V48,F11 V51,F11 V52,F11 V53,F11 V54,F11 V79,F17 V80,F17 V84,F18 V85,F18 V86,F18 V87,F18 V88,F18 NOTE: SAMPLE COVARIANCE MATRIX AND RESIDUA TECHNICAL OUTPUT HAVE BEEN ARRANGED IN T OF ALL DEPENDENT VARIABLES FOLLOWED BY A VARIABLES 22 ELEMENTS OF MODEL STATISTICS, THEY ARE 85 ELEMENTS OF PARAMETER ESTIMATES 85 ELEMENTS OF STANDARD E 85 ELEMENTS OF ROBUST STANDARD ERRORS 5 LINES OF INFORMATION FOR DEPENDENT VAR 6 LINES OF INFORMATION FOR INDEPENDENT V OUTPUT FORMAT FOR INFORMATION SECTION IS: (8E16.8 TOTAL NUMBER OF LINES PER SET OF INFORMATI MATRIX GFI-ML MAY NOT BE POSITIVE DEFINITE.

114

Appendix F: EQS output � English sample - adjusted single-group model run

with permission of the EQS copyright holder Peter M. Bentler. IATE SOFTWARE, INC.

- 1998.

H34.EDS

=ML,ROBUST;

Q13; V18=Q14A; V19=Q14B; V20=Q14C;

Q32A; V48=Q32B; V49=Q32C; V50=Q32D;

B; V77=Q48; V78=Q49; V79=Q50; V80=Q51;

E43 ;

10 + 1.000 E44 ;

11 + 1.000 E47 ;

The EQS code is printed EQS, A STRUCTURAL EQUATION PROGRAM MULTIVAR COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) 1985 PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389 3 /SPECIFICATIONS 4 DATA='C:\THESIS\EN_IMP~1\T_EIMP2.ESS'; 5 VARIABLES= 89; CASES= 459; 6 METHODS 7 MATRIX=RAW; 8 /LABELS 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q12B; 12 V16=Q12C; V17= 13 V21=Q14D; V22=Q14E; V23=Q15; V24=Q16; V25=Q17; 14 V26=Q18; V27=Q19; V28=Q20; V29=Q21; V30=Q22; 15 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 17 V41=Q31B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F; 18 V46=Q31G; V47= 19 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 20 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V60=Q41; 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43D; 22 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 24 V76=Q47 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q58; V88=Q59; V89=Q12AB_CO; 27 /EQUATIONS 28 V16 = 1.162*F4 + 1.000 E16 ; 29 V17 = 1.000 F5 + 1.000 E17 ; 30 V18 = 1.042*F5 + 1.000 E18 ; 31 V19 = 1.063*F5 + 1.000 E19 ; 32 V20 = 1.007*F5 + 1.000 E20 ; 33 V21 = 1.158*F5 + 1.000 E21 ; 34 V22 = 1.097*F5 + 1.000 E22 ; 35 V40 = 1.000 F10 + 1.000 E40 ; 36 V41 = 1.030*F10 + 1.000 E41 ; 37 V42 = .998*F10 + 1.000 E42 ; 38 V43 = 1.078*F10 + 1.000 39 V44 = .927*F 40 V45 = 1.044*F10 + 1.000 E45 ; 41 V46 = 1.121*F10 + 1.000 E46 ; 42 V47 = 1.000 F 43 V48 = 1.886*F11 + 1.000 E48 ; 44 V49 = 2.026*F11 + 1.000 E49 ; 45 V50 = 2.101*F11 + 1.000 E50 ; 46 V51 = 2.728*F11 + 1.000 E51 ; 47 V52 = 2.467*F11 + 1.000 E52 ; 48 V53 = 2.928*F11 + 1.000 E53 ; 49 V54 = 2.802*F11 + 1.000 E54 ; 50 V78 = 1.000 F17 + 1.000 E78 ; 51 V79 = 1.671*F17 + 1.000 E79 ; 52 V80 = 1.806*F17 + 1.000 E80 ;

115

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS 03/16/03 PAGE : 2

18 + 1.000 E83 ;

4 + 1.000 E89 ;

18= .677* ;

11,F4 = .083* ;

11,F10 = .138* ;

N_IMP~1\5F389H34.EDS 03/16/03 PAGE : 3

17,F10 = .237* ;

18,F4 = .238* ;

18,F10 = .400* ;

118 F18,F17 = .269* ; 119 /LMTEST 120 PROCESS=SIMULTANEOUS; 121 SET=PVV,PFV,PFF,PEE,PDD,GVV,GVF,GFV,GFF,BVF,BFF; 122 /WTEST 123 PVAL=0.05; 124 PRIORITY=ZERO; 124 RECORDS OF INPUT MODEL FILE WERE READ DATA IS READ FROM C:\THESIS\EN_IMP~1\T_EIMP2.ESS THERE ARE 89 VARIABLES AND 459 CASES IT IS A RAW DATA ESS FILE

EQS/EM386 Licensee: Mikhail Koulikov 53 V81 = 1.743*F17 + 1.000 E81 ; 54 V82 = 1.000 F18 + 1.000 E82 ; 55 V83 = .887*F 56 V84 = 1.046*F18 + 1.000 E84 ; 57 V85 = .990*F18 + 1.000 E85 ; 58 V86 = 1.059*F18 + 1.000 E86 ; 59 V87 = 1.206*F18 + 1.000 E87 ; 60 V88 = .819*F18 + 1.000 E88 ; 61 V89 = 1.000 F 62 /VARIANCES 63 F4= .431* ; 64 F5= .369* ; 65 F10= .853* ; 66 F11= .091* ; 67 F17= .204* ; 68 F 69 E16= .084* ; 70 E17= .771* ; 71 E18= .791* ; 72 E19= .617* ; 73 E20= .366* ; 74 E21= .303* ; 75 E22= .463* ; 76 E40= .531* ; 77 E41= .363* ; 78 E42= .412* ; 79 E43= .348* ; 80 E44= .379* ; 81 E45= .458* ; 82 E46= .295* ; 83 E47= .498* ; 84 E48= .374* ; 85 E49= .347* ; 86 E50= .336* ; 87 E51= .471* ; 88 E52= .461* ; 89 E53= .516* ; 90 E54= .350* ; 91 E78= .376* ; 92 E79= .316* ; 93 E80= .441* ; 94 E81= .301* ; 95 E82= .278* ; 96 E83= .466* ; 97 E84= .460* ; 98 E85= .267* ; 99 E86= .171* ; 100 E87= .295* ; 101 E88= .332* ; 102 E89= .105* ; 103 /COVARIANCES 104 F5,F4 = .196* ; 105 F10,F4 = .140* ; 106 F10,F5 = .229* ; 107 F 108 F11,F5 = .135* ; 109 F TITLE: Model created by EQS 5.7b -- C:\THESIS\E EQS/EM386 Licensee: Mikhail Koulikov 110 F17,F4 = .097* ; 111 F17,F5 = .147* ; 112 F 113 F17,F11 = .080* ; 114 F 115 F18,F5 = .345* ; 116 F 117 F18,F11 = .198* ;

116

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS 03/16/03 PAGE : 4 EQS/EM386 Licens SAMPLE STATISTICS BASED ON COMPLETE CASES

NESS (G1) 1.2849 0.2462 0.6930 0.7470 1.0043 IS (G2) 2.7548 -0.5398 -0.3705 -0.0670 1.1043

0918 1.0172 0.8604

Q31C

65

44

610

50

ATE KURTOSIS = 0.1433 = 0.2889

ALIZED MULTIVARIATE KURTOSIS:

CASE NUMBER 35 272 281 355 451 ESTIMATE 2345.8423 1590.5983 1690.5610 1755.8904 1740.4422

ee: Mikhail Koulikov

UNIVARIATE STATISTICS --------------------- VARIABLE Q12C Q13 Q14A Q14B Q14C MEAN 2.0463 2.9955 2.7257 2.6569 2.5213 SKEW KURTOS STANDARD DEV. 0.8161 1.0676 1. VARIABLE Q14D Q14E Q31A Q31B MEAN 2.6796 2.6714 2.4633 2.4120 2.5485 SKEWNESS (G1) 0.7471 0.6618 0.6807 0.6057 0.52 KURTOSIS (G2) 0.4712 0.2002 -0.2444 -0.3215 -0.3329 STANDARD DEV. 0.8929 0.9523 1.1762 1.1261 1.1231 VARIABLE Q31D Q31E Q31F Q31G Q32A MEAN 2.4653 2.2348 2.6058 2.4699 2.27 SKEWNESS (G1) 0.6306 0.7215 0.3958 0.5201 0.8855 KURTOSIS (G2) -0.3384 0.0126 -0.6201 -0.5117 2.0641 STANDARD DEV. 1.1571 1.0548 1.1777 1.1690 0.7673 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.4521 2.4240 2.3980 2.9835 2.7671 SKEWNESS (G1) 0.8656 0.9184 0.8323 0.3758 0.4300 KURTOSIS (G2) 1.0557 1.2368 1.1149 -0.5042 -0.1731 STANDARD DEV. 0.8343 0.8484 0.8579 1.0705 1.0065 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.9610 2.6752 1.8093 2.0446 2.1336 SKEWNESS (G1) 0.3007 0.5288 1.2328 1.0135 1.0844 KURTOSIS (G2) -0.6359 0.0936 2.8132 1.0842 0.8864 STANDARD DEV. 1.1372 1.0307 0.7615 0.9405 1.0511 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3489 2.4360 2.5698 2.5774 2.2873 SKEWNESS (G1) 0.7493 0.5879 0.7131 0.5796 0.7 KURTOSIS (G2) 0.7948 0.1410 0.2351 -0.1173 0.5959 STANDARD DEV. 0.9588 0.9773 0.9994 1.0955 0.96 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.3955 2.6554 2.6039 1.9840 SKEWNESS (G1) 0.6048 0.4939 0.3295 1.0128 KURTOSIS (G2) 0.3116 -0.3478 0.3661 2.4074 STANDARD DEV. 0.9649 1.1314 0.8864 0.7320 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 353.5560 NORMALIZED ESTIMATE = 76.5471 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.2889 MEAN SCALED UNIVARI MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORM ---------------------------------------------------------------------------

117


SELECTED FROM 89 VARIABLES) BASED ON 459 CASES.[OMMITTED]

5

40 41 42 50 51 52 83 84 85

ORDS OF MEMORY.

1\5F389H34.EDS 03/16/03 PAGE : 6

BUTION THEORY)


1\5F389H34.EDS 03/16/03 PAGE : 7

BUTION THEORY)

NG OPTIMIZATION.

1\5F389H34.EDS 03/16/03 PAGE : 10

BUTION THEORY)

N 561 DEGREES OF FREEDOM

AIC = 7431.56175 0429

FREEDOM C IS LESS THAN 0.001

LUTION IS 1554.642.

UARE STATISTIC IS 0.00000

CTION

EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 34 VARIABLES ( BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V'S : 16 17 18 19 20 21 22 40 41 42 DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 52 DEPENDENT V'S : 53 54 78 79 80 81 82 83 84 8 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 40 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 INDEPENDENT E'S : 43 44 45 46 47 48 49 INDEPENDENT E'S : 53 54 78 79 80 81 82 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 83 NUMBER OF FIXED NONZERO PARAMETERS = 40 3RD STAGE OF COMPUTATION REQUIRED 328206 W PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IS 0.43722E-11 TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI CASE CONTRIBUTION TO PARAMETER VARIANCES (I TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI PARAMETER ESTIMATES APPEAR IN ORDER, NO SPECIAL PROBLEMS WERE ENCOUNTERED DURI [OMMISSION UNTIL P. 10 OF THE OUTPUT] TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI GOODNESS OF FIT SUMMARY INDEPENDENCE MODEL CHI-SQUARE = 11430.959 O INDEPENDENCE AIC = 10308.95892 INDEPENDENCE C MODEL AIC = 451.76942 MODEL CAIC = -2174.3 CHI-SQUARE = 1475.769 BASED ON 512 DEGREES OF PROBABILITY VALUE FOR THE CHI-SQUARE STATISTI THE NORMAL THEORY RLS CHI-SQUARE FOR THIS ML SO SATORRA-BENTLER SCALED CHI-SQUARE = 1118.7881 PROBABILITY VALUE FOR THE CHI-SQ BENTLER-BONETT NORMED FIT INDEX= 0.871 BENTLER-BONETT NONNORMED FIT INDEX= 0.903 COMPARATIVE FIT INDEX (CFI) = 0.911 ROBUST COMPARATIVE FIT INDEX = 0.919 ITERATIVE SUMMARY PARAMETER ITERATION ABS CHANGE ALPHA FUN 1 0.000418 1.00000 3.22220 [OMITTED UNTIL P. 22 OF THE OUTPUT]

118

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS 03/16/03 PAGE : 22 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

ALD TEST (FOR DROPPING PARAMETERS) THIS WALD TEST

IMULTANEOUS PROCESS

IATE INCREMENT

-SQUARE PROBABILITY

IS PROCESS.

34.EDS 03/16/03 PAGE : 23 Licensee: Mikhail Koulikov

N THEORY)

MEMORY.

LAGRANGE MULTIPLIER TEST (FOR ADDING PARAMETERS)

RAMETER CHANGE

ITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS 03/16/03 PAGE : 24

TION THEORY)

NEOUS PROCESS IN STAGE 1

SUBMATRICES) ACTIVE AT THIS STAGE ARE:

BFF

UNIVARIATE INCREMENT

TEP PARAMETER CHI-SQUARE D.F. PROBABILITY CHI-SQUARE PROBABILITY --------

18 139.341 1 0.000 139.341 0.000 0.000

.891 0.000

40.479 0.000

ecution begins at 18:53:01.50

W ROBUST INFORMATION MATRIX USED IN MULTIVARIATE WALD TEST BY S CUMULATIVE MULTIVARIATE STATISTICS UNIVAR ---------------------------------- -------------------- STEP PARAMETER CHI-SQUARE D.F. PROBABILITY CHI ---- ----------- ---------- ---- ----------- ---------- ----------- ************ NONE OF THE FREE PARAMETERS IS DROPPED IN TH TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H EQS/EM386 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTIO LAGRANGE MULTIPLIER TEST REQUIRES 730019 WORDS OF PROGRAM ALLOCATES 900000 WORDS. ORDERED UNIVARIATE TEST STATISTICS: NO CODE PARAMETER CHI-SQUARE PROBABILITY PA -- ---- --------- ---------- ----------- ---------------- 1 2 12 V81,F18 139.341 0.000 0.836 2 2 6 E19,E18 64.561 0.000 0.293 3 2 6 E50,E48 56.891 0.000 0.139 [THE REST IS OMITTED] T EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBU MULTIVARIATE LAGRANGE MULTIPLIER TEST BY SIMULTA PARAMETER SETS ( PVV PFV PFF PEE PDD GVV GVF GFV GFF BVF CUMULATIVE MULTIVARIATE STATISTICS ---------------------------------- -------------------- S ---- ----------- ---------- ---- ----------- ---------- --- 1 V81,F 2 E19,E18 203.903 2 0.000 64.561 3 E50,E48 260.793 3 0.000 56 4 E49,E48 319.817 4 0.000 59.024 0.000 5 E50,E49 368.578 5 0.000 48.761 0.000 6 E41,E40 409.058 6 0.000 7 E48,E47 446.023 7 0.000 36.965 0.000 8 E49,E47 474.846 8 0.000 28.823 0.000 9 E42,E41 501.378 9 0.000 26.532 0.000 [THE REST IS OMITTED] Ex Execution ends at 18:53:09.46 Elapsed time = 7.96 seconds

119

Appendix G: EQS output � English sample - final baseline model run

rmission of the EQS copyright holder Peter M. Bentler.

TE SOFTWARE, INC. 5 - 1998.

ESS'; IABLES= 89; CASES= 459;

LABELS

Q8; 0; V13=Q11; V14=Q12A; V15=Q12B;

=Q14C; Q14E; V23=Q15; V24=Q16; V25=Q17; 19; V28=Q20; V29=Q21; V30=Q22;

30; V40=Q31A; B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F;

34; V53=Q35; V54=Q36; V55=Q37A;

43A; V63=Q43B; V64=Q43C; V65=Q43D;

Q46C; V75=Q47A;

53; V83=Q54; V84=Q55; V85=Q56;

10 + 1.000 E40 ; 41 ;

10 + 1.000 E42 ;

10 + 1.000 E44 ;

11 + 1.000 E47 ;

The EQS code is printed with pe EQS, A STRUCTURAL EQUATION PROGRAM MULTIVARIA COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) 198 PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 3 /SPECIFICATIONS 4 DATA='C:\THESIS\EN_IMP~1\T_EIMP2. 5 VAR 6 METHODS=ML,ROBUST; 7 MATRIX=RAW; 8 / 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10= 11 V11=Q9; V12=Q1 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V20 13 V21=Q14D; V22= 14 V26=Q18; V27=Q 15 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q 17 V41=Q31 18 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50=Q32D; 19 V51=Q33; V52=Q 20 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V60=Q41; 21 V61=Q42; V62=Q 22 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 23 V71=Q45; V72=Q46A; V73=Q46B; V74= 24 V76=Q47B; V77=Q48; V78=Q49; V79=Q50; V80=Q51; 25 V81=Q52; V82=Q 26 V86=Q57; V87=Q58; V88=Q59; V89=Q12AB_CO; 27 /EQUATION 28 V16 = 1.160*F4 + 1.000 E16 ; 29 V17 = 1.000 F5 + 1.000 E17 ; 30 V18 = 1.042*F5 + 1.000 E18 ; 31 V19 = 1.064*F5 + 1.000 E19 ; 32 V20 = 1.007*F5 + 1.000 E20 ; 33 V21 = 1.158*F5 + 1.000 E21 ; 34 V22 = 1.097*F5 + 1.000 E22 ; 35 V40 = 1.000 F 36 V41 = 1.030*F10 + 1.000 E 37 V42 = .998*F 38 V43 = 1.079*F10 + 1.000 E43 ; 39 V44 = .928*F 40 V45 = 1.044*F10 + 1.000 E45 ; 41 V46 = 1.122*F10 + 1.000 E46 ; 42 V47 = 1.000 F 43 V48 = 1.890*F11 + 1.000 E48 ; 44 V49 = 2.031*F11 + 1.000 E49 ; 45 V50 = 2.106*F11 + 1.000 E50 ; 46 V51 = 2.734*F11 + 1.000 E51 ; 47 V52 = 2.470*F11 + 1.000 E52 ; 48 V53 = 2.935*F11 + 1.000 E53 ; 49 V54 = 2.809*F11 + 1.000 E54 ; 50 V78 = 1.000 F17 + 1.000 E78 ; 51 V79 = 1.696*F17 + 1.000 E79 ; 52 V80 = 1.793*F17 + 1.000 E80 ;

120

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 2

17 + .572*F18 + 1.000 E81 ;

18 + 1.000 E85 ;

18 + 1.000 E88 ;

4= .432* ;

* ;

11= .090* ; 17= .225* ;

F389I34.EDS 05/24/03 PAGE : 3 Licensee: Mikhail Koulikov

17,F5 = .132* ;

18,F5 = .344* ; 116 F18,F10 = .401* ; 117 F18,F11 = .197* ; 118 F18,F17 = .230* ; 119 /LMTEST 120 PROCESS=SIMULTANEOUS; 121 SET=PVV,PFV,PFF,PEE,PDD,GVV,GVF,GFV,GFF,BVF,BFF; 122 /WTEST 123 PVAL=0.05; 124 PRIORITY=ZERO; 124 RECORDS OF INPUT MODEL FILE WERE READ DATA IS READ FROM C:\THESIS\EN_IMP~1\T_EIMP2.ESS THERE ARE 89 VARIABLES AND 459 CASES IT IS A RAW DATA ESS FILE

EQS/EM386 Licensee: Mikhail Koulikov 53 V81 = .886*F 54 V82 = 1.000 F18 + 1.000 E82 ; 55 V83 = .884*F18 + 1.000 E83 ; 56 V84 = 1.047*F18 + 1.000 E84 ; 57 V85 = .987*F 58 V86 = 1.061*F18 + 1.000 E86 ; 59 V87 = 1.205*F18 + 1.000 E87 ; 60 V88 = .817*F 61 V89 = 1.000 F4 + 1.000 E89 ; 62 /VARIANCES 63 F 64 F5= .369 65 F10= .852* ; 66 F 67 F 68 F18= .679* ; 69 E16= .085* ; 70 E17= .771* ; 71 E18= .791* ; 72 E19= .617* ; 73 E20= .366* ; 74 E21= .303* ; 75 E22= .463* ; 76 E40= .531* ; 77 E41= .364* ; 78 E42= .412* ; 79 E43= .348* ; 80 E44= .379* ; 81 E45= .457* ; 82 E46= .295* ; 83 E47= .498* ; 84 E48= .374* ; 85 E49= .347* ; 86 E50= .335* ; 87 E51= .471* ; 88 E52= .462* ; 89 E53= .515* ; 90 E54= .349* ; 91 E78= .355* ; 92 E79= .238* ; 93 E80= .382* ; 94 E81= .287* ; 95 E82= .276* ; 96 E83= .468* ; 97 E84= .456* ; 98 E85= .270* ; 99 E86= .167* ; 100 E87= .295* ; 101 E88= .333* ; 102 E89= .104* ; 103 /COVARIANCES 104 F5,F4 = .196* ; 105 F10,F4 = .140* ; 106 F10,F5 = .229* ; 107 F11,F4 = .083* ; 108 F11,F5 = .134* ; 109 F11,F10 = .138* ; TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5 EQS/EM386 110 F17,F4 = .089* ; 111 F 112 F17,F10 = .229* ; 113 F17,F11 = .069* ; 114 F18,F4 = .236* ; 115 F

121

TITLE: Model created by EQS/EM386 Licensee: Mi SAMPLE STATISTICS BASED ON COMPLETE CASES

UNIVARIATE STATISTICS ---------------------

IABLE Q12C Q13 Q14A Q14B Q14C

930 0.7470 1.0043 IS (G2) 2.7548 -0.5398 -0.3705 -0.0670 1.1043

2 0.8604

231

0


= 0.2889 CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORMALIZED MULTIVARIATE KURTOSIS: --------------------------------------------------------------------------- CASE NUMBER 35 272 281 355 451 ESTIMATE 2345.8423 1590.5983 1690.5610 1755.8904 1740.4422

EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 4 khail Koulikov

VAR MEAN 2.0463 2.9955 2.7257 2.6569 2.5213 SKEWNESS (G1) 1.2849 0.2462 0.6 KURTOS STANDARD DEV. 0.8161 1.0676 1.0918 1.017 VARIABLE Q14D Q14E Q31A Q31B Q31C MEAN 2.6796 2.6714 2.4633 2.4120 2.5485 SKEWNESS (G1) 0.7471 0.6618 0.6807 0.6057 0.5265 KURTOSIS (G2) 0.4712 0.2002 -0.2444 -0.3215 -0.3329 STANDARD DEV. 0.8929 0.9523 1.1762 1.1261 1.1 VARIABLE Q31D Q31E Q31F Q31G Q32A MEAN 2.4653 2.2348 2.6058 2.4699 2.2744 SKEWNESS (G1) 0.6306 0.7215 0.3958 0.5201 0.8855 KURTOSIS (G2) -0.3384 0.0126 -0.6201 -0.5117 2.0641 STANDARD DEV. 1.1571 1.0548 1.1777 1.1690 0.7673 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.4521 2.4240 2.3980 2.9835 2.7671 SKEWNESS (G1) 0.8656 0.9184 0.8323 0.3758 0.4300 KURTOSIS (G2) 1.0557 1.2368 1.1149 -0.5042 -0.1731 STANDARD DEV. 0.8343 0.8484 0.8579 1.0705 1.0065 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.9610 2.6752 1.8093 2.0446 2.1336 SKEWNESS (G1) 0.3007 0.5288 1.2328 1.0135 1.0844 KURTOSIS (G2) -0.6359 0.0936 2.8132 1.0842 0.8864 STANDARD DEV. 1.1372 1.0307 0.7615 0.9405 1.0511 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3489 2.4360 2.5698 2.5774 2.2873 SKEWNESS (G1) 0.7493 0.5879 0.7131 0.5796 0.7610 KURTOSIS (G2) 0.7948 0.1410 0.2351 -0.1173 0.5959 STANDARD DEV. 0.9588 0.9773 0.9994 1.0955 0.965 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.3955 2.6554 2.6039 1.9840 SKEWNESS (G1) 0.6048 0.4939 0.3295 1.0128 KURTOSIS (G2) 0.3116 -0.3478 0.3661 2.4074 STANDARD DEV. 0.9649 1.1314 0.8864 0.7320 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 353.5560 NORMALIZED ESTIMATE = 76.5471 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.2889 MEAN SCALED UNIVARI MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA

122


ZED: 34 VARIABLES (SELECTED FROM 89 VARIABLES) [OMTTED]

5

40 41 42 50 51 52 83 84 85

ORDS OF MEMORY.

1\5F389I34.EDS 05/24/03 PAGE : 6

BUTION THEORY)


1\5F389I34.EDS 05/24/03 PAGE : 7

BUTION THEORY)

NG OPTIMIZATION. [OMITTED]

DUALS = 0.0318 CE RESIDUALS = 0.0337

1\5F389I34.EDS 05/24/03 PAGE : 8

BUTION THEORY)

UALS = 0.0325 IDUALS = 0.0345

LARGEST STANDARDIZED RESIDUALS:

EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALY BASED ON 459 CASES. BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V'S : 16 17 18 19 20 21 22 40 41 42 DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 52 DEPENDENT V'S : 53 54 78 79 80 81 82 83 84 8 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 40 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 INDEPENDENT E'S : 43 44 45 46 47 48 49 INDEPENDENT E'S : 53 54 78 79 80 81 82 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 84 NUMBER OF FIXED NONZERO PARAMETERS = 40 3RD STAGE OF COMPUTATION REQUIRED 332075 W PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IS 0.43722E-11 TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI CASE CONTRIBUTION TO PARAMETER VARIANCES (I TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI PARAMETER ESTIMATES APPEAR IN ORDER, NO SPECIAL PROBLEMS WERE ENCOUNTERED DURI RESIDUAL COVARIANCE MATRIX (S-SIGMA) [OMITTED] AVERAGE ABSOLUTE COVARIANCE RESI AVERAGE OFF-DIAGONAL ABSOLUTE COVARIAN TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI STANDARDIZED RESIDUAL MATRIX [OMITTED] AVERAGE ABSOLUTE STANDARDIZED RESID AVERAGE OFF-DIAGONAL ABSOLUTE STANDARDIZED RES V 19,V 18 V 48,V 47 V 50,V 48 V 44,V 19 V 49,V 48 0.211 0.160 0.156 -0.152 0.144 V 88,V 54 V 43,V 17 V 45,V 17 V 79,V 47 V 49,V 47 0.141 0.137 0.133 0.129 0.117 V 49,V 19 V 53,V 20 V 82,V 54 V 44,V 17 V 82,V 47 -0.112 -0.105 0.105 0.105 -0.103 V 53,V 18 V 45,V 22 V 50,V 49 V 78,V 43 V 85,V 54 0.101 0.100 0.100 0.098 0.098

123

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 9 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

LS

0 0.00%

0 0.00%

C EACH "*" REPRESENTS 16 RESIDUALS

34.EDS 05/24/03 PAGE : 10

THEORY)

DEGREES OF FREEDOM

NDEPENDENCE AIC = 10308.95892 INDEPENDENCE CAIC = 7431.56175 92.57636

F FREEDOM TIC IS LESS THAN 0.001 L SOLUTION IS 1399.811.

ENTLER SCALED CHI-SQUARE = 1029.0965 RE STATISTIC IS 0.00000

ENTLER-BONETT NORMED FIT INDEX= 0.882 0.915

923 UST COMPARATIVE FIT INDEX = 0.931

TERATION ABS CHANGE ALPHA FUNCTION

DISTRIBUTION OF STANDARDIZED RESIDUA ---------------------------------------- ! ! 320- * - ! * ! ! * ! ! * * ! ! * * ! RANGE FREQ PERCENT 240- * * - ! * * ! 1 -0.5 - -- 0 0.00% ! * * ! 2 -0.4 - -0.5 0 0.00% ! * * ! 3 -0.3 - -0.4 0 0.00% ! * * ! 4 -0.2 - -0.3 160- * * - 5 -0.1 - -0.2 4 0.67% ! * * ! 6 0.0 - -0.1 315 52.94% ! * * ! 7 0.1 - 0.0 264 44.37% ! * * ! 8 0.2 - 0.1 11 1.85% ! * * ! 9 0.3 - 0.2 1 0.17% 80- * * - A 0.4 - 0.3 ! * * ! B 0.5 - 0.4 0 0.00% ! * * ! C ++ - 0.5 0 0.00% ! * * ! ------------------------------- ! * * * ! TOTAL 595 100.00% ---------------------------------------- 1 2 3 4 5 6 7 8 9 A B TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION GOODNESS OF FIT SUMMARY INDEPENDENCE MODEL CHI-SQUARE = 11430.959 ON 561 I MODEL AIC = 328.36830 MODEL CAIC = -22 CHI-SQUARE = 1350.368 BASED ON 511 DEGREES O PROBABILITY VALUE FOR THE CHI-SQUARE STATIS THE NORMAL THEORY RLS CHI-SQUARE FOR THIS M SATORRA-B PROBABILITY VALUE FOR THE CHI-SQUA B BENTLER-BONETT NONNORMED FIT INDEX= COMPARATIVE FIT INDEX (CFI) = 0. ROB ITERATIVE SUMMARY PARAMETER I 1 0.000421 1.00000 2.94840

124

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 11 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS

OBUST STATISTICS IN PARENTHESES)

*F5 + 1.000 E18

=V19 = 1.064*F5 + 1.000 E19

*F5 + 1.000 E21

=V41 = 1.030*F10 + 1.000 E41

*F10 + 1.000 E43

( .050) ( 18.526) MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS (CONTINUED)

(R Q12C =V16 = 1.159*F4 + 1.000 E16 .069 16.905 ( .086) ( 13.417) Q13 =V17 = 1.000 F5 + 1.000 E17 Q14A =V18 = 1.043 .107 9.787 ( .104) ( 10.020) Q14B .102 10.422 ( .098) ( 10.801) Q14C =V20 = 1.007*F5 + 1.000 E20 .090 11.202 ( .091) ( 11.056) Q14D =V21 = 1.158 .097 11.879 ( .092) ( 12.549) Q14E =V22 = 1.097*F5 + 1.000 E22 .099 11.096 ( .094) ( 11.627) Q31A =V40 = 1.000 F10 + 1.000 E40 Q31B .051 20.371 ( .043) ( 23.849) Q31C =V42 = .998*F10 + 1.000 E42 .051 19.608 ( .051) ( 19.597) Q31D =V43 = 1.079 .052 20.886 ( .054) ( 20.039) Q31E =V44 = .928*F10 + 1.000 E44 .048 19.341

125

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 12 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) (ROBUST STATISTICS IN PARENTHESES)

=V45 = 1.045*F10 + 1.000 E45

*F11 + 1.000 E50

E51

F11 + 1.000 E52

F11 + 1.000 E54

Q50 =V79 = 1.695*F17 + 1.000 E79 .124 13.620 ( .172) ( 9.854) MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS (CONTINUED)

Q31F .053 19.549 ( .057) ( 18.175) Q31G =V46 = 1.122*F10 + 1.000 E46 .052 21.715 ( .048) ( 23.431) Q32A =V47 = 1.000 F11 + 1.000 E47 Q32B =V48 = 1.890*F11 + 1.000 E48 .239 7.923 ( .305) ( 6.195) Q32C =V49 = 2.031*F11 + 1.000 E49 .252 8.059 ( .345) ( 5.881) Q32D =V50 = 2.107 .260 8.119 ( .350) ( 6.012) Q33 =V51 = 2.736*F11 + 1.000 .333 8.209 ( .470) ( 5.819) Q34 =V52 = 2.471* .304 8.118 ( .424) ( 5.826) Q35 =V53 = 2.937*F11 + 1.000 E53 .357 8.232 ( .502) ( 5.848) Q36 =V54 = 2.812* .337 8.347 ( .480) ( 5.861) Q49 =V78 = 1.000 F17 + 1.000 E78

126

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 13 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) (ROBUST STATISTICS IN PARENTHESES)

F17 + 1.000 E80

F17 + .572*F18 + 1.000 E81

20.546

( .048)

( 25.079)

I34.EDS 05/24/03 PAGE : 14

DISTRIBUTION THEORY)

I F4 - F4 .432*I

I .059 I

I 9.907 I

I ( .030)I I ( 2.997)I

I I

I 6.948 I I ( .044)I I ( 5.063)I I I

I F18 - F18 .679*I I .061 I I 11.083 I I ( .065)I I ( 10.494)I

Q51 =V80 = 1.792* .135 13.287 ( .193) ( 9.272)

Q52 =V81 = .886* .098 .047 9.078 12.078 ( .131) ( .062) ( 6.772) ( 9.214)

Q53 =V82 = 1.000 F18 + 1.000 E82

Q54 =V83 = .884*F18 + 1.000 E83 .048

18.300 ( .053) ( 16.579)

Q55 =V84 = 1.047*F18 + 1.000 E84 .051 ( .054) ( 19.433)

Q56 =V85 = .987*F18 + 1.000 E85 .043 22.934 ( 20.583)

Q57 =V86 = 1.061*F18 + 1.000 E86 .041 26.076 ( .042)

Q58 =V87 = 1.205*F18 + 1.000 E87 .049 24.616 ( .050)

( 23.901)

Q59 =V88 = .817*F18 + 1.000 E88 .042 19.433 ( .047) ( 17.233)

12AB_CO=V89 = 1.000 F4 + 1.000 E89 Q TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL VARIANCES OF INDEPENDENT VARIABLES V F I .041 I I 10.433 I I ( .058)I I ( 7.419)I I I

I F5 - F5 .369*I I 6.234 I I ( .056)I I ( 6.552)I I I

I F10 - F10 .852*I I .086 I I ( .081)I I ( 10.459)I I I

I F11 - F11 .090*I I .022 I I 4.192 I

I F17 - F17 .225*I I .032 I

127

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.031 I I

.055 I I

.057 I I

.045 I I

.029 I I

.027 I I

.036 I I

.039 I I

.028 I I

.031 I I

.028 I I

.028 I I

I I I I

I I

---------------------------------- E D --- --- E16 - Q12C .086*I I 2.773 I I ( .034)I I ( 2.484)I I I I E17 - Q13 .771*I I 14.032 I I ( .056)I I ( 13.790)I I I I E18 - Q14A .791*I I 13.966 I I ( .060)I I ( 13.163)I I I I E19 - Q14B .617*I I 13.572 I I ( .052)I I ( 11.896)I I I I E20 - Q14C .366*I I 12.761 I I ( .035)I I ( 10.577)I I I I E21 - Q14D .303*I I 11.318 I I ( .031)I I ( 9.839)I I I I E22 - Q14E .463*I I 12.906 I I ( .045)I I ( 10.200)I I I I E40 - Q31A .532*I I 13.704 I I ( .053)I I ( 10.019)I I I I E41 - Q31B .363*I I 12.905 I I ( .034)I I ( 10.647)I I I I E42 - Q31C .412*I I 13.291 I I ( .036)I I ( 11.384)I I I I E43 - Q31D .348*I I 12.575 I I ( .039)I I ( 8.981)I I I I E44 - Q31E .379*I I 13.404 ( .043) ( 8.833)I I

128


MAL DISTRIBUTION THEORY)

CONTINUED)

5*I I I I

EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NOR VARIANCES OF INDEPENDENT VARIABLES ( ---------------------------------------------- E45 - Q31F .457*I I .034 I I 13.317 I I ( .047)I I ( 9.633)I I I I E46 - Q31G .295*I I .025 I I 11.868 I I ( .030)I I ( 9.887)I I I I E47 - Q32A .499*I I .033 I I 14.896 I I ( .043)I I ( 11.471)I I I I E48 - Q32B .374*I I .027 I I 13.997 I I ( .030)I I ( 12.299)I I I I E49 - Q32C .347*I I .025 I I 13.720 I I ( .033)I I ( 10.683)I I I I E50 - Q32D .336*I I .025 I I 13.557 I I ( .027)I I ( 12.531)I I I I E51 - Q33 .471*I I .036 I I

13.236 I I ( .040)I I

( 11.777)I I I I E52 - Q34 .462*I I .034 I I 13.559 I I ( .046)I I ( 10.109)I I I I E53 - Q35 .51 .039 13.135 I I ( .049)I I ( 10.409)I I

I

I E54 - Q36 .349*I I .028 I I 12.423 I I ( .035)I I ( 9.876)I I I I E78 - Q49 .355*I I .026 I I 13.624 I I ( .040)I I ( 8.791)I I I I E79 - Q50 .238*I I .028 I I 8.379 I I ( .039)I I ( 6.190)I I I I

129

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---------

I I

------------------------------------- E80 - Q51 .383*I .037 I 10.293 I I ( .059)I I ( 6.438)I I I I E81 - Q52 .287*I I .022 I I 13.064 I I ( .027)I I ( 10.550)I I I I E82 - Q53 .276*I I .021 I I 13.235 I I ( .037)I I ( 7.376)I I I I E83 - Q54 .468*I I .033 I I 14.264 I I ( .051)I I ( 9.129)I I I I E84 - Q55 .457*I I .033 I I 13.880 I I ( .042)I I ( 10.781)I I I I E85 - Q56 .270*I I .020 I I 13.246 I I ( .029)I I ( 9.433)I I I I E86 - Q57 .167*I I .014 I I 11.596 I I ( .018)I I ( 9.525)I I I I E87 - Q58 .295*I I .023 I I 12.542 I I ( .031)I I ( 9.616)I I I I E88 - Q59 .333*I I .024 I I 14.089 I I ( .026)I I ( 13.046)I I I I E89 -Q12AB_CO .103*I I .024 I I 4.379 I I ( .035)I I ( 2.988)I I I I

130



OVARIANCES AMONG INDEPENDENT VARIABLES

V F

I I

EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NOR C --------------------------------------- --- --- I F5 - F5 .196*I I F4 - F4 .028 I I 7.044 I I ( .033)I I ( 5.997)I I I I F10 - F10 .140*I I F4 - F4 .032 I I 4.367 I I ( .035)I I ( 4.050)I I I I F11 - F11 .083*I I F4 - F4 .015 I I 5.638 I I ( .019)I I ( 4.342)I I I I F17 - F17 .089*I I F4 - F4 .018 I I 4.887 I I ( .020)I I ( 4.432)I I I I F18 - F18 .237*I I F4 - F4 .031 I I 7.553 I I ( .036)I I ( 6.601)I I I I F10 - F10 .229*I I F5 - F5 .036 I I 6.331 I I ( .039)I I ( 5.908)I I I I F11 - F11 .134*I I F5 - F5 .021 I I 6.288 I I ( .025)I I ( 5.467)I I I I F17 - F17 .132*I I F5 - F5 .021 I I 6.293 I I ( .025)I I ( 5.344)I I I I F18 - F18 .343*I I F5 - F5 .040 I I 8.536 I I ( .043)I I ( 8.080)I I I I F11 - F11 .138*I I F10 - F10 .023 I I 6.112 I I ( .027)I I ( 5.124)I I I I F17 - F17 .229*I I F10 - F10 .030 I I 7.630 I I ( .036)I I ( 6.282)I I I I F18 - F18 .401*I I F10 - F10 .046 I I 8.725 I I ( .047)I I ( 8.486)I

131

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-

EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 20

L DISTRIBUTION THEORY)

R-SQUARED

.872 .324

.336 .403

.505 .620 .490

.616 .713

.673 .740

.659 .670

.784 .153

.463 .517

.544 .589

.544 .602

.671 .388

.731 .654

1 .688 1

.531 .620 .710 .820

.770 Q59 =V88 = .759*F18 + .651 E88 .576 Q12AB_CO=V89 = .898 F4 + .439 E89 .807

-------------------------------------------------- I F17 - F17 .069*I I F11 - F11 .012 I I 5.693 I I ( .016)I I ( 4.418)I I I I F18 - F18 .197*I I F11 - F11 .028 I I 7.087 I I ( .033)I I ( 5.999)I I I I F18 - F18 .230*I I F17 - F17 .028 I I 8.269 I I ( .031)I I ( 7.315)I I I TITLE: Model created by EQS 5.7b -- C:\THESIS\ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMA STANDARDIZED SOLUTION: Q12C =V16 = .934*F4 + .358 E16 Q13 =V17 = .569 F5 + .822 E17 Q14A =V18 = .580*F5 + .815 E18 Q14B =V19 = .635*F5 + .772 E19 Q14C =V20 = .711*F5 + .703 E20 Q14D =V21 = .788*F5 + .616 E21 Q14E =V22 = .700*F5 + .714 E22 Q31A =V40 = .785 F10 + .620 E40 Q31B =V41 = .845*F10 + .535 E41 Q31C =V42 = .821*F10 + .572 E42 Q31D =V43 = .860*F10 + .509 E43 Q31E =V44 = .812*F10 + .584 E44 Q31F =V45 = .819*F10 + .574 E45 Q31G =V46 = .886*F10 + .464 E46 Q32A =V47 = .391 F11 + .920 E47 Q32B =V48 = .680*F11 + .733 E48 Q32C =V49 = .719*F11 + .695 E49 Q32D =V50 = .738*F11 + .675 E50 Q33 =V51 = .768*F11 + .641 E51 Q34 =V52 = .737*F11 + .675 E52 Q35 =V53 = .776*F11 + .631 E53 Q36 =V54 = .819*F11 + .573 E54 Q49 =V78 = .623 F17 + .783 E78 Q50 =V79 = .855*F17 + .519 E79 Q51 =V80 = .809*F17 + .588 E80 Q52 =V81 = .438*F17 + .492*F18 + .558 E8 Q53 =V82 = .843 F18 + .538 E82 .71 Q54 =V83 = .729*F18 + .685 E83 Q55 =V84 = .787*F18 + .617 E84 Q56 =V85 = .842*F18 + .539 E85 Q57 =V86 = .906*F18 + .424 E86 Q58 =V87 = .877*F18 + .480 E87

132

TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389I34.EDS 05/24/03 PAGE : 21 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) CORRELATIONS AMONG INDEPENDENT VARIABLES

I I I

.230*I I

--------------

----------------- MP~1\5F389I34.EDS 05/24/03 PAGE : 22


S WALD TEST EOUS PROCESS

UNIVARIATE INCREMENT ---

BILITY CHI-SQUARE PROBABILITY ---------

ROPPED IN THIS PROCESS.

IMP~1\5F389I34.EDS 05/24/03 PAGE : 23

L DISTRIBUTION THEORY)

WORDS OF MEMORY.

ARAMETERS)

:

OBABILITY PARAMETER CHANGE -

.293 2 2 6 E50,E48 56.893 0.000 0.139 3 2 12 V54,F18 50.058 0.000 0.533 4 2 6 E49,E48 45.223 0.000 0.125 5 2 6 E41,E40 40.762 0.000 0.154 6 2 6 E45,E40 32.852 0.000 -0.151 7 2 6 E53,E50 31.955 0.000 -0.128 8 2 6 E48,E47 28.301 0.000 0.113 9 2 6 E52,E51 27.222 0.000 0.132 10 2 6 E50,E49 26.545 0.000 0.093 [THE REST IS OMMITTED]

I F5 - F5 .491*I I F4 - F4 I F10 - F10 I F4 - F4 I I I F11 - F11 .418*I I F4 - F4 I I I I F17 - F17 .284*I I F4 - F4 I I I I F18 - F18 .437*I I F4 - F4 I I I I F10 - F10 .408*I I F5 - F5 I I I I F11 - F11 .735*I I F5 - F5 I I I I F17 - F17 .460*I I F5 - F5 I I I I F18 - F18 .686*I I F5 - F5 I I I I F11 - F11 .497*I I F10 - F10 I I I I F17 - F17 .523*I I F10 - F10 I I I I F18 - F18 .527*I I F10 - F10 I I I I F17 - F17 .486*I I F11 - F11 I I I I F18 - F18 .797*I I F11 - F11 I I I I F18 - F18 .589*I I F17 - F17 I ----------------------------------------------------------------- E N D O F M E T H O D -------------------------------------------------------------- TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_I EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL WALD TEST (FOR DROPPING PARAMETERS) ROBUST INFORMATION MATRIX USED IN THI MULTIVARIATE WALD TEST BY SIMULTAN CUMULATIVE MULTIVARIATE STATISTICS ---------------------------------- ----------------- STEP PARAMETER CHI-SQUARE D.F. PROBA ---- ----------- ---------- ---- ----------- ---------- -- ************ NONE OF THE FREE PARAMETERS IS D TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_ EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMA LAGRANGE MULTIPLIER TEST REQUIRES 730028 PROGRAM ALLOCATES 900000 WORDS. LAGRANGE MULTIPLIER TEST (FOR ADDING P ORDERED UNIVARIATE TEST STATISTICS NO CODE PARAMETER CHI-SQUARE PR -- ---- --------- ---------- ----------- --------------- 1 2 6 E19,E18 64.565 0.000 0

133



LTIVARIATE LAGRANGE MULTIPLIER TEST BY SIMULTANEOUS PROCESS IN STAGE 1

FF BVF BFF CUMULATIVE MULTIVARIATE STATISTICS UNIVARIATE INCREMENT

TY CHI-SQUARE PROBABILITY --

0.000 0.000 0.000

35 0.000 0.000 0.000

0.000 0.000 0.000

680 0.000 0.000 0.000

4 0.000 0 0.000 8 0.000

705 0.000

12.476 0.000

17 598.346 22 0.000 11.944 0.001

11 640.123 26 0.000 9.914 0.002

61 E78,E44 903.887 61 0.000 7.883 0.005 62 E88,E80 909.168 62 0.000 5.281 0.022 63 E84,E53 914.422 63 0.000 5.254 0.022 64 E87,E42 919.637 64 0.000 5.215 0.022 65 E88,E84 924.802 65 0.000 5.165 0.023 66 E85,E20 929.865 66 0.000 5.062 0.024 67 E53,E50 934.399 67 0.000 4.534 0.033 68 E82,E80 938.828 68 0.000 4.429 0.035 69 E85,E43 943.126 69 0.000 4.298 0.038 70 V19,F10 947.404 70 0.000 4.278 0.039 71 E89,E42 951.630 71 0.000 4.227 0.040 72 E51,E17 955.719 72 0.000 4.089 0.043 73 E53,E20 959.915 73 0.000 4.196 0.041 74 E53,E21 964.771 74 0.000 4.856 0.028 75 E85,E53 969.093 75 0.000 4.322 0.038 76 E44,E17 973.090 76 0.000 3.997 0.046 77 E44,E16 977.218 77 0.000 4.129 0.042 78 E89,E48 981.185 78 0.000 3.967 0.046 1 Execution begins at 12:37:09.20 Execution ends at 12:37:18.21 Elapsed time = 9.01 seconds

EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NOR MU PARAMETER SETS (SUBMATRICES) ACTIVE AT THIS STAGE ARE: PVV PFV PFF PEE PDD GVV GVF GFV G STEP PARAMETER CHI-SQUARE D.F. PROBABILI ---- ----------- ---------- ---- ----------- ---------- --------- 1 E19,E18 64.565 1 0.000 64.565 2 E50,E48 121.458 2 0.000 56.893 3 E49,E48 180.486 3 0.000 59.028 4 E50,E49 229.121 4 0.000 48.6 5 E41,E40 269.883 5 0.000 40.762 6 E48,E47 307.035 6 0.000 37.152 7 V54,F18 336.108 7 0.000 29.073 8 E49,E47 363.255 8 0.000 27.147 9 E42,E41 389.990 9 0.000 26.735 10 E45,E40 413.669 10 0.000 23. 11 E50,E47 435.917 11 0.000 22.247 12 E54,E53 455.217 12 0.000 19.300 13 E89,E44 471.480 13 0.000 16.26 14 E53,E18 487.491 14 0.000 16.01 15 E53,E51 503.749 15 0.000 16.25 16 E52,E51 519.454 16 0.000 15. 17 E42,E40 534.346 17 0.000 14.892 0.000 18 V18,F18 549.190 18 0.000 14.844 0.000 19 E87,E53 561.666 19 0.000 20 E20,E19 574.055 20 0.000 12.388 0.000 21 E21,E20 586.401 21 0.000 12.347 0.000 22 V47,F 23 E53,E52 609.375 23 0.000 11.029 0.001 24 E49,E19 620.204 24 0.000 10.829 0.001 25 E50,E41 630.209 25 0.000 10.006 0.002 26 V88,F 27 V83,F5 650.047 27 0.000 9.924 0.002 28 E83,E82 660.306 28 0.000 10.259 0.001 29 E81,E40 669.986 29 0.000 9.680 0.002 30 E88,E52 679.434 30 0.000 9.449 0.002 31 E81,E52 689.633 31 0.000 10.198 0.001 32 V79,F10 698.317 32 0.000 8.684 0.003 33 E83,E53 706.798 33 0.000 8.481 0.004 34 V17,F10 715.257 34 0.000 8.459 0.004 35 E88,E41 723.609 35 0.000 8.352 0.004 36 E82,E78 731.910 36 0.000 8.301 0.004 37 E78,E54 740.140 37 0.000 8.230 0.004 38 E78,E53 751.349 38 0.000 11.209 0.001 39 E89,E17 759.167 39 0.000 7.818 0.005 40 E48,E42 766.943 40 0.000 7.776 0.005 41 E85,E81 774.263 41 0.000 7.321 0.007 42 E44,E42 781.366 42 0.000 7.103 0.008 43 V44,F5 788.197 43 0.000 6.831 0.009 44 E49,E18 794.981 44 0.000 6.784 0.009 45 E41,E17 801.683 45 0.000 6.702 0.010 46 E80,E41 808.310 46 0.000 6.627 0.010 47 E81,E41 816.787 47 0.000 8.477 0.004 48 E81,E47 823.185 48 0.000 6.398 0.011 49 E82,E47 830.074 49 0.000 6.889 0.009 50 E82,E45 836.051 50 0.000 5.977 0.014 51 E43,E22 841.944 51 0.000 5.893 0.015 52 E45,E22 847.792 52 0.000 5.849 0.016 53 E47,E17 853.417 53 0.000 5.625 0.018 54 E84,E82 859.005 54 0.000 5.588 0.018 55 E87,E82 866.921 55 0.000 7.916 0.005 56 E80,E19 872.483 56 0.000 5.563 0.018 57 E79,E17 877.965 57 0.000 5.482 0.019 58 E79,E42 883.412 58 0.000 5.447 0.020 59 E78,E43 889.222 59 0.000 5.809 0.016 60 E78,E45 896.004 60 0.000 6.783 0.009

134

Appendix H: EQS output � Spanish sample - initial single-group model run

th permission of the EQS copyright holder Peter M. Bentler.

MULTIVARIATE SOFTWARE, INC. (C) 1985 - 1998.

';

12B; ; V20=Q14C;

6; V25=Q17; 22;

5=Q27A; V40=Q31A;

45=Q31F; ; V50=Q32D;

Q41;

V65=Q43D;

75=Q47A; 0=Q51;

0*F11 + 1.000 E50 ;

The EQS code is printed wi EQS, A STRUCTURAL EQUATION PROGRAM COPYRIGHT BY P.M. BENTLER VERSION 5.7b PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model created by EQS 5.7b -- 5F389B34.EDS 3 /SPECIFICATIONS 4 DATA='C:\THESIS\SP_IMP~1\T_SIMP.ESS 5 VARIABLES= 88; CASES= 525; 6 METHODS=ML,ROBUST; 7 MATRIX=RAW; 8 /LABELS 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B 13 V21=Q14D; V22=Q14E; V23=Q15; V24=Q1 14 V26=Q18; V27=Q19; V28=Q20; V29=Q21; V30=Q 15 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V3 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; 17 V41=Q31B; V42=Q31C; V43=Q31D; V44=Q31E; V 18 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C 19 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 20 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V60= 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; 22 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V 24 V76=Q47B; V77=Q48; V78=Q49; V79=Q50; V8 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q58; V88=Q59; 27 /EQUATIONS 28 V14 = 1.000 F4 + 1.000 E14 ; 29 V15 = .978*F4 + 1.000 E15 ; 30 V16 = 1.000*F4 + 1.000 E16 ; 31 V17 = 1.000 F5 + 1.000 E17 ; 32 V18 = 1.222*F5 + 1.000 E18 ; 33 V19 = 1.392*F5 + 1.000 E19 ; 34 V20 = 1.496*F5 + 1.000 E20 ; 35 V21 = 1.473*F5 + 1.000 E21 ; 36 V22 = 1.308*F5 + 1.000 E22 ; 37 V40 = 1.000 F10 + 1.000 E40 ; 38 V41 = .992*F10 + 1.000 E41 ; 39 V42 = 1.029*F10 + 1.000 E42 ; 40 V43 = 1.022*F10 + 1.000 E43 ; 41 V44 = .967*F10 + 1.000 E44 ; 42 V45 = 1.065*F10 + 1.000 E45 ; 43 V46 = 1.062*F10 + 1.000 E46 ; 44 V47 = 1.000 F11 + 1.000 E47 ; 45 V48 = 1.128*F11 + 1.000 E48 ; 46 V49 = .933*F11 + 1.000 E49 ; 47 V50 = 1.09 48 V51 = 1.442*F11 + 1.000 E51 ; 49 V52 = 1.209*F11 + 1.000 E52 ; 50 V53 = 1.357*F11 + 1.000 E53 ; 51 V54 = 1.284*F11 + 1.000 E54 ; 52 V78 = 1.000 F17 + 1.000 E78 ;

135

TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 2

03/16/03 PAGE : 3

BFF;

EQS/EM386 Licensee: Mikhail Koulikov 53 V79 = 1.703*F17 + 1.000 E79 ; 54 V80 = 1.657*F17 + 1.000 E80 ; 55 V81 = 1.306*F17 + 1.000 E81 ; 56 V82 = 1.000 F18 + 1.000 E82 ; 57 V83 = .961*F18 + 1.000 E83 ; 58 V84 = 1.002*F18 + 1.000 E84 ; 59 V85 = 1.109*F18 + 1.000 E85 ; 60 V86 = 1.115*F18 + 1.000 E86 ; 61 V87 = 1.074*F18 + 1.000 E87 ; 62 V88 = .930*F18 + 1.000 E88 ; 63 /VARIANCES 64 F4= .523* ; 65 F5= .249* ; 66 F10= .788* ; 67 F11= .284* ; 68 F17= .158* ; 69 F18= .490* ; 70 E14= .193* ; 71 E15= .166* ; 72 E16= .206* ; 73 E17= .842* ; 74 E18= .444* ; 75 E19= .608* ; 76 E20= .348* ; 77 E21= .348* ; 78 E22= .491* ; 79 E40= .541* ; 80 E41= .420* ; 81 E42= .349* ; 82 E43= .294* ; 83 E44= .381* ; 84 E45= .392* ; 85 E46= .449* ; 86 E47= .363* ; 87 E48= .320* ; 88 E49= .273* ; 89 E50= .312* ; 90 E51= .410* ; 91 E52= .420* ; 92 E53= .464* ; 93 E54= .390* ; 94 E78= .347* ; 95 E79= .364* ; 96 E80= .419* ; 97 E81= .342* ; 98 E82= .318* ; 99 E83= .294* ; 100 E84= .352* ; 101 E85= .240* ; 102 E86= .211* ; 103 E87= .450* ; 104 E88= .402* ; 105 /COVARIANCES 106 F5,F4 = .212* ; 107 F10,F4 = .271* ; 108 F10,F5 = .224* ; 109 F11,F4 = .199* ; TITLE: Model created by EQS 5.7b -- 5F389B34.EDS EQS/EM386 Licensee: Mikhail Koulikov 110 F11,F5 = .175* ; 111 F11,F10 = .295* ; 112 F17,F4 = .122* ; 113 F17,F5 = .102* ; 114 F17,F10 = .182* ; 115 F17,F11 = .131* ; 116 F18,F4 = .232* ; 117 F18,F5 = .185* ; 118 F18,F10 = .334* ; 119 F18,F11 = .247* ; 120 F18,F17 = .221* ; 121 /LMTEST 122 PROCESS=SIMULTANEOUS; 123 SET=PVV,PFV,PFF,PEE,PDD,GVV,GVF,GFV,GFF,BVF, 124 /WTEST 125 PVAL=0.05; 126 PRIORITY=ZERO; 126 RECORDS OF INPUT MODEL FILE WERE READ DATA IS READ FROM C:\THESIS\SP_IMP~1\T_SIMP.ESS THERE ARE 88 VARIABLES AND 525 CASES IT IS A RAW DATA ESS FILE

136

TITLE: Model created by EQS 5.7b -- 5F389B34.EDS 03/16/03 PAGE : 4 EQS/EM386 License SAMPLE STATISTICS BASED ON COMPLETE CASES

---------------------

NESS (G1) 1.3499 1.2848 1.3985 0.5789 1.3159

2

9

MEAN 2.3733 2.3060 2.3230 2.2865 2.3224 SKEWNESS (G1) 0.9521 0.9534 1.0268 1.0461 0.8830 KURTOSIS (G2) 0.9820 0.8901 2.5957 1.4682 1.0330 STANDARD DEV. 0.9068 0.9239 0.7822 0.8985 0.8639 VARIABLE Q55 Q56 Q57 Q58 Q59 MEAN 2.3883 2.2631 2.2925 2.5839 2.4669 SKEWNESS (G1) 0.8746 1.0328 0.9909 0.6077 0.6812 KURTOSIS (G2) 1.0066 1.4612 1.3322 0.0920 1.0825 STANDARD DEV. 0.9187 0.9183 0.9055 1.0076 0.9085

e: Mikhail Koulikov

UNIVARIATE STATISTICS VARIABLE Q12A Q12B Q12C Q13 Q14A MEAN 2.1217 2.1573 2.1507 2.6965 2.2259 SKEW KURTOSIS (G2) 2.5798 2.5602 2.7584 -0.1181 2.0173 STANDARD DEV. 0.8462 0.8161 0.8539 1.0444 0.9032 VARIABLE Q14B Q14C Q14D Q14E Q31A MEAN 2.5646 2.4495 2.5498 2.4848 2.7844 SKEWNESS (G1) 0.8385 0.9846 0.9363 1.0160 0.4517 KURTOSIS (G2) 0.1466 0.8513 0.7918 0.9186 -0.5617 STANDARD DEV. 1.0443 0.9514 0.9424 0.9577 1.1528 VARIABLE Q31B Q31C Q31D Q31E Q31F MEAN 2.8403 2.7817 2.5928 2.5833 2.7067 SKEWNESS (G1) 0.3553 0.4452 0.6608 0.6852 0.4822 KURTOSIS (G2) -0.5108 -0.3858 -0.0746 -0.0535 -0.4412 STANDARD DEV. 1.0936 1.0878 1.0572 1.0575 1.1338 VARIABLE Q31G Q32A Q32B Q32C Q32D MEAN 2.6083 2.1950 2.2059 2.2187 2.2424 SKEWNESS (G1) 0.5899 1.2481 1.1602 1.0786 1.0245 KURTOSIS (G2) -0.3680 2.7520 2.4002 2.7845 2.0389 STANDARD DEV. 1.1565 0.8046 0.8254 0.7217 0.806 VARIABLE Q33 Q34 Q35 Q36 Q49 MEAN 2.4951 2.4678 2.5848 2.3638 1.8959 SKEWNESS (G1) 0.9294 0.8618 0.7768 0.9320 1.2934 KURTOSIS (G2) 0.6864 0.9433 0.4351 1.1587 3.6936 STANDARD DEV. 1.0005 0.9143 0.9941 0.9271 0.710 VARIABLE Q50 Q51 Q52 Q53 Q54

137

MULTIVARIATE KURTOSIS


= 0.4721

NORMALIZED MULTIVARIATE KURTOSIS:

15

3013 3702.4082

03/16/03 PAGE : 5

BLES (SELECTED FROM 88 VARIABLES)

22 40 49 50 82 83

21 22 40 48 49 50 81 82 83

RDS OF MEMORY.

--------------------- MARDIA'S COEFFICIENT (G2,P) = 611.3390 NORMALIZED ESTIMATE = 137.6201 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.4721 MEAN SCALED UNIVARI MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA CASE NUMBERS WITH LARGEST CONTRIBUTION TO --------------------------------------------------------------------------- CASE NUMBER 5 148 236 511 5 ESTIMATE 5518.2960 2890.7464 3116.8651 3052. TITLE: Model created by EQS 5.7b -- 5F389B34.EDS EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 35 VARIA BASED ON 525 CASES. [OMITTED] BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 35 DEPENDENT V'S : 14 15 16 17 18 19 20 21 DEPENDENT V'S : 41 42 43 44 45 46 47 48 DEPENDENT V'S : 51 52 53 54 78 79 80 81 DEPENDENT V'S : 84 85 86 87 88 NUMBER OF INDEPENDENT VARIABLES = 41 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 14 15 16 17 18 19 20 INDEPENDENT E'S : 41 42 43 44 45 46 47 INDEPENDENT E'S : 51 52 53 54 78 79 80 INDEPENDENT E'S : 84 85 86 87 88 NUMBER OF FREE PARAMETERS = 85 NUMBER OF FIXED NONZERO PARAMETERS = 41 3RD STAGE OF COMPUTATION REQUIRED 354818 WO PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IS 0.67731E-12

138

Appendix I: EQS output � Spanish sample final baseline model run

with permission of the EQS copyright holder Peter M. Bentler.

, A STRUCTURAL EQUATION PROGRAM MULTIVARIATE SOFTWARE, INC. 1985 - 1998.

ated by EQS 5.7b -- 5F389I34.EDS

'C:\THESIS\SP_IMP~1\T_SIMP2.ESS';

=ML,ROBUST; IX=RAW;

ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4;

0; V13=Q11; V14=Q12A; V15=Q12B;

Q14E; V23=Q15; V24=Q16; V25=Q17;

24; V33=Q25; V34=Q26; V35=Q27A;

B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F; Q32A; V48=Q32B; V49=Q32C; V50=Q32D;

B; V57=Q38; V58=Q39; V59=Q40; V60=Q41;

D; Q43F; V68=Q43G; V69=Q43H; V70=Q44;

B; V77=Q48; V78=Q49; V79=Q50; V80=Q51;

58; V88=Q59; V89=Q12AB_CO;

5 + 1.000 E17 ;

10 + 1.000 E40 ;

10 + 1.000 E44 ;

11 + 1.000 E47 ;

11 + 1.000 E49 ;

17 + 1.000 E78 ;

The EQS code is printed EQS COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model cre 3 /SPECIFICATIONS 4 DATA= 5 VARIABLES= 89; CASES= 524; 6 METHODS 7 MATR 8 /LABELS 9 V1= 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q1 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V20=Q14C; 13 V21=Q14D; V22= 14 V26=Q18; V27=Q19; V28=Q20; V29=Q21; V30=Q22; 15 V31=Q23; V32=Q 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 17 V41=Q31 18 V46=Q31G; V47= 19 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 20 V56=Q37 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43 22 V66=Q43E; V67= 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 24 V76=Q47 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q 27 /EQUATION 28 V16 = 1.006*F4 + 1.000 E16 ; 29 V17 = 1.000 F 30 V18 = 1.207*F5 + 1.000 E18 ; 31 V19 = 1.394*F5 + 1.000 E19 ; 32 V20 = 1.500*F5 + 1.000 E20 ; 33 V21 = 1.460*F5 + 1.000 E21 ; 34 V22 = 1.310*F5 + 1.000 E22 ; 35 V40 = 1.000 F 36 V41 = 1.000*F10 + 1.000 E41 ; 37 V42 = 1.029*F10 + 1.000 E42 ; 38 V43 = 1.030*F10 + 1.000 E43 ; 39 V44 = .967*F 40 V45 = 1.065*F10 + 1.000 E45 ; 41 V46 = 1.070*F10 + 1.000 E46 ; 42 V47 = 1.000 F 43 V48 = 1.152*F11 + 1.000 E48 ; 44 V49 = .933*F 45 V50 = 1.113*F11 + 1.000 E50 ; 46 V51 = 1.450*F11 + 1.000 E51 ; 47 V52 = 1.231*F11 + 1.000 E52 ; 48 V53 = 1.364*F11 + 1.000 E53 ; 49 V54 = 1.310*F11 + 1.000 E54 ; 50 V78 = 1.000 F 51 V79 = 1.812*F17 + 1.000 E79 ; 52 V80 = 1.756*F17 + 1.000 E80 ;

139

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 2

Licensee: Mikhail Koulikov

18 + 1.000 E83 ; 18 + 1.000 E84 ;

4 + 1.000 E89 ;

4= .507* ; 5= .250* ;

17= .155* ;

.379* ;

IANCES

10,F5 = .223* ;

108 F11,F5 = .172* ; 109 F11,F10 = .289* ; TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 3 EQS/EM386 Licensee: Mikhail Koulikov 110 F17,F4 = .105* ; 111 F17,F5 = .093* ; 112 F17,F10 = .165* ; 113 F17,F11 = .120* ; 114 F18,F4 = .226* ; 115 F18,F5 = .184* ; 116 F18,F10 = .329* ; 117 F18,F11 = .240* ; 118 F18,F17 = .196* ; 119 /LMTEST 120 PROCESS=SIMULTANEOUS; 121 SET=PVV,PFV,PFF,PEE,PDD,GVV,GVF,GFV,GFF,BVF,BFF; 122 /WTEST 123 PVAL=0.05; 124 PRIORITY=ZERO; 125 /END 125 RECORDS OF INPUT MODEL FILE WERE READ DATA IS READ FROM C:\THESIS\SP_IMP~1\T_SIMP2.ESS THERE ARE 89 VARIABLES AND 524 CASES IT IS A RAW DATA ESS FILE

EQS/EM386 53 V81 = .540*F17 + .483*F18 + 1.000 E81 ; 54 V82 = 1.000 F18 + 1.000 E82 ; 55 V83 = .972*F 56 V84 = .997*F 57 V85 = 1.124*F18 + 1.000 E85 ; 58 V86 = 1.115*F18 + 1.000 E86 ; 59 V87 = 1.089*F18 + 1.000 E87 ; 60 V88 = .927*F18 + 1.000 E88 ; 61 V89 = 1.000 F 62 /VARIANCES 63 F 64 F 65 F10= .782* ; 66 F11= .277* ; 67 F 68 F18= .483* ; 69 E16= .202* ; 70 E17= .843* ; 71 E18= .438* ; 72 E19= .606* ; 73 E20= .344* ; 74 E21= .345* ; 75 E22= .489* ; 76 E40= .540* ; 77 E41= .417* ; 78 E42= .348* ; 79 E43= .290* ; 80 E44= 81 E45= .391* ; 82 E46= .446* ; 83 E47= .357* ; 84 E48= .316* ; 85 E49= .267* ; 86 E50= .309* ; 87 E51= .410* ; 88 E52= .419* ; 89 E53= .464* ; 90 E54= .386* ; 91 E78= .333* ; 92 E79= .316* ; 93 E80= .364* ; 94 E81= .353* ; 95 E82= .312* ; 96 E83= .291* ; 97 E84= .353* ; 98 E85= .235* ; 99 E86= .208* ; 100 E87= .444* ; 101 E88= .400* ; 102 E89= .092* ; 103 /COVAR 104 F5,F4 = .209* ; 105 F10,F4 = .264* ; 106 F 107 F11,F4 = .192* ;

140

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 4 EQS/EM386 Licensee: Mikh SAMPLE STATISTICS BASED ON COMPLETE CASES

ARIABLE Q12C Q13 Q14A Q14B Q14C

IS (G2) 2.7887 -0.1237 2.0481 0.1406 0.8440 8959 1.0453 0.9523

Q31C

55

97

0.9173

2

MARDIA-BASED KAPPA = 0.4507 MEAN SCALED UNIVARIATE KURTOSIS = 0.3548 MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA= 0.4507 CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORMALIZED MULTIVARIATE KURTOSIS: --------------------------------------------------------------------------- CASE NUMBER 147 206 235 510 514 ESTIMATE 2968.2113 2661.4384 3206.6996 3151.3172 3803.2697

ail Koulikov

UNIVARIATE STATISTICS --------------------- V MEAN 2.1452 2.6965 2.2206 2.5646 2.4495 SKEWNESS (G1) 1.3891 0.5783 1.3119 0.8376 0.9837 KURTOS STANDARD DEV. 0.8455 1.0454 0. VARIABLE Q14D Q14E Q31A Q31B MEAN 2.5451 2.4848 2.7802 2.8403 2.7775 SKEWNESS (G1) 0.9346 1.0151 0.4534 0.3549 0.44 KURTOSIS (G2) 0.8134 0.9112 -0.5514 -0.5155 -0.3754 STANDARD DEV. 0.9372 0.9587 1.1498 1.0947 1.0845 VARIABLE Q31D Q31E Q31F Q31G Q32A MEAN 2.5928 2.5787 2.7023 2.6083 2.18 SKEWNESS (G1) 0.6602 0.6850 0.4831 0.5893 1.2281 KURTOSIS (G2) -0.0802 -0.0407 -0.4307 -0.3730 2.7548 STANDARD DEV. 1.0582 1.0532 1.1304 1.1576 0.7959 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.2059 2.2134 2.2424 2.4903 2.4678 SKEWNESS (G1) 1.1591 1.0335 1.0235 0.9294 0.8609 KURTOSIS (G2) 2.3899 2.7005 2.0292 0.7101 0.9357 STANDARD DEV. 0.8262 0.7121 0.8069 0.9954 0.9152 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.5802 2.3638 1.8900 2.3733 2.3009 SKEWNESS (G1) 0.7756 0.9311 1.2237 0.9512 0.9446 KURTOSIS (G2) 0.4536 1.1508 3.4898 0.9744 0.8905 STANDARD DEV. 0.9894 0.9280 0.6985 0.9077 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3230 2.2813 2.3224 2.3833 2.2631 SKEWNESS (G1) 1.0258 1.0365 0.8822 0.8659 1.0318 KURTOSIS (G2) 2.5850 1.4789 1.0254 1.0156 1.4527 STANDARD DEV. 0.7830 0.8915 0.8647 0.9125 0.919 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.2874 2.5839 2.4621 2.1368 SKEWNESS (G1) 0.9808 0.6071 0.6698 1.2458 KURTOSIS (G2) 1.3401 0.0862 1.0939 2.5796 STANDARD DEV. 0.8986 1.0086 0.9026 0.7738 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 551.6557 NORMALIZED ESTIMATE = 127.6139 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------

141


ZED: 34 VARIABLES (SELECTED FROM 89 VARIABLES)

2 2

4 85

40 41 42 50 51 52 83 84 85

ORDS OF MEMORY.

05/24/03 PAGE : 6

BUTION THEORY)

N DESCENDING ORDER)

05/24/03 PAGE : 7

BUTION THEORY)

NG OPTIMIZATION.

ALS = 0.0333 CE RESIDUALS = 0.0353

05/24/03 PAGE : 8

UTION THEORY)

UALS = 0.0372 IDUALS = 0.0394

8,V 17 126 0.126

EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALY BASED ON 524 CASES. [OMTTED] BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V'S : 16 17 18 19 20 21 22 40 41 4 DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 5 DEPENDENT V'S : 53 54 78 79 80 81 82 83 8 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 40 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 INDEPENDENT E'S : 43 44 45 46 47 48 49 INDEPENDENT E'S : 53 54 78 79 80 81 82 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 84 NUMBER OF FIXED NONZERO PARAMETERS = 40 3RD STAGE OF COMPUTATION REQUIRED 332075 W PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IS 0.15676E-11 TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI CASE CONTRIBUTION TO PARAMETER VARIANCES (I [OMTTED] TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRI PARAMETER ESTIMATES APPEAR IN ORDER, NO SPECIAL PROBLEMS WERE ENCOUNTERED DURI RESIDUAL COVARIANCE MATRIX (S-SIGMA) : [OMTTED] AVERAGE ABSOLUTE COVARIANCE RESIDU AVERAGE OFF-DIAGONAL ABSOLUTE COVARIAN TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIB STANDARDIZED RESIDUAL MATRIX: [OMTTED] AVERAGE ABSOLUTE STANDARDIZED RESID AVERAGE OFF-DIAGONAL ABSOLUTE STANDARDIZED RES LARGEST STANDARDIZED RESIDUALS: V 46,V 17 V 52,V 17 V 40,V 17 V 87,V 17 V 44,V 17 0.235 0.208 0.199 0.175 0.172 V 83,V 17 V 79,V 17 V 87,V 53 V 45,V 17 V 41,V 17 0.169 0.160 0.160 0.153 0.150 V 43,V 17 V 54,V 17 V 53,V 17 V 51,V 17 V 42,V 17 0.148 0.147 0.147 0.145 0.144 V 89,V 17 V 81,V 17 V 54,V 53 V 87,V 46 V 8 0.139 0.139 0.133 0.

142

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 9 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

IZED RESIDUALS

0 0.00%

98 50.08%

8 4.71%

0 0.00%

0 0.00%

L 595 100.00%

SIDUALS

05/24/03 PAGE : 10

THEORY)

DEGREES OF FREEDOM

39.47366 L AIC = 418.94661 MODEL CAIC = -2269.67564

EES OF FREEDOM TIC IS LESS THAN 0.001 L SOLUTION IS 1550.044.

ENTLER SCALED CHI-SQUARE = 962.2215 ABILITY VALUE FOR THE CHI-SQUARE STATISTIC IS 0.00000

FIT INDEX= 0.876 ENTLER-BONETT NONNORMED FIT INDEX= 0.908

TERATION ABS CHANGE ALPHA FUNCTION

DISTRIBUTION OF STANDARD ---------------------------------------- ! ! 300- * - ! * ! ! * * ! ! * * ! ! * * ! RANGE FREQ PERCENT 225- * * - ! * * ! 1 -0.5 - -- 0 0.00% ! * * ! 2 -0.4 - -0.5 ! * * ! 3 -0.3 - -0.4 0 0.00% ! * * ! 4 -0.2 - -0.3 0 0.00% 150- * * - 5 -0.1 - -0.2 1 0.17% ! * * ! 6 0.0 - -0.1 2 ! * * ! 7 0.1 - 0.0 266 44.71% ! * * ! 8 0.2 - 0.1 2 ! * * ! 9 0.3 - 0.2 2 0.34% 75- * * - A 0.4 - 0.3 ! * * ! B 0.5 - 0.4 0 0.00% ! * * ! C ++ - 0.5 ! * * * ! ------------------------------- ! * * * ! TOTA ---------------------------------------- 1 2 3 4 5 6 7 8 9 A B C EACH "*" REPRESENTS 15 RE TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION GOODNESS OF FIT SUMMARY INDEPENDENCE MODEL CHI-SQUARE = 11613.170 ON 561 INDEPENDENCE AIC = 10491.17050 INDEPENDENCE CAIC = 75 MODE CHI-SQUARE = 1440.947 BASED ON 511 DEGR PROBABILITY VALUE FOR THE CHI-SQUARE STATIS THE NORMAL THEORY RLS CHI-SQUARE FOR THIS M SATORRA-B PROB BENTLER-BONETT NORMED B COMPARATIVE FIT INDEX (CFI) = 0.916 ROBUST COMPARATIVE FIT INDEX = 0.932 ITERATIVE SUMMARY PARAMETER I 1 0.000297 1.00000 2.75516

143


SOLUTION (NORMAL DISTRIBUTION THEORY)

UREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS

=V19 = 1.395*F5 + 1.000 E19

85

F5 + 1.000 E22 31

F10 + 1.000 E40

=V41 = .999*F10 + 1.000 E41

735)

*F10 + 1.000 E43

S AND TEST STATISTICS (CONTINUED)

EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD MEAS (ROBUST STATISTICS IN PARENTHESES) Q12C =V16 = 1.006*F4 + 1.000 E16 .056 18.106 ( .079) ( 12.765) Q13 =V17 = 1.000 F5 + 1.000 E17 Q14A =V18 = 1.207*F5 + 1.000 E18 .122 9.907 ( .153) ( 7.871) Q14B .141 9.863 ( .167) ( 8.376) Q14C =V20 = 1.501*F5 + 1.000 E20 .142 10.5 ( .164) ( 9.178) Q14D =V21 = 1.461*F5 + 1.000 E21 .139 10.542 ( .169) ( 8.652) Q14E =V22 = 1.311* .1 9.977 ( .155) ( 8.452) Q31A =V40 = 1.000 Q31B .050 19.856 ( .046) ( 21. Q31C =V42 = 1.029*F10 + 1.000 E42 .049 20.827 ( .046) ( 22.508) Q31D =V43 = 1.030 .048 21.502 ( .048) ( 21.299) Q31E =V44 = .967*F10 + 1.000 E44 .048 19.983 ( .050) ( 19.264) MEASUREMENT EQUATIONS WITH STANDARD ERROR

144


IMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) OBUST STATISTICS IN PARENTHESES)

F11 + 1.000 E51

+ 1.000 E78

Q50 =V79 = 1.812*F17 + 1.000 E79 .154 11.803 ( .223) ( 8.134) MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS (CONTINUED)

EQS/EM386 Licensee: Mikhail Koulikov MAX (R Q31F =V45 = 1.065*F10 + 1.000 E45 .052 20.641 ( .052) ( 20.605) Q31G =V46 = 1.070*F10 + 1.000 E46 .053 20.145 ( .056) ( 19.104) Q32A =V47 = 1.000 F11 + 1.000 E47 Q32B =V48 = 1.152*F11 + 1.000 E48 .078 14.725 ( .089) ( 12.961) Q32C =V49 = .933*F11 + 1.000 E49 .067 13.962 ( .081) ( 11.497) Q32D =V50 = 1.113*F11 + 1.000 E50 .076 14.590 ( .095) ( 11.695) Q33 =V51 = 1.450* .095 15.273 ( .122) ( 11.930) Q34 =V52 = 1.231*F11 + 1.000 E52 .086 14.288 ( .122) ( 10.108) Q35 =V53 = 1.364*F11 + 1.000 E53 .094 14.592 ( .143) ( 9.564) Q36 =V54 = 1.310*F11 + 1.000 E54 .088 14.884 ( .121) ( 10.872) Q49 =V78 = 1.000 F17

145

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 13 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) (ROBUST STATISTICS IN PARENTHESES)

81

( 3.669) ( 6.376)

( 17.006)

( 13.825)

( 14.249)

( 16.861)

( 13.898)

( 13.577)

Q51 =V80 = 1.756*F17 + 1.000 E80 .151 11.618 ( .207) ( 8.503) Q52 =V81 = .540*F17 + .483*F18 + 1.000 E .133 .070 4.067 6.945 ( .147) ( .076) Q53 =V82 = 1.000 F18 + 1.000 E82 Q54 =V83 = .972*F18 + 1.000 E83 .050 19.309 ( .057) Q55 =V84 = .997*F18 + 1.000 E84 .054 18.625 ( .072) Q56 =V85 = 1.124*F18 + 1.000 E85 .052 21.457 ( .079) Q57 =V86 = 1.115*F18 + 1.000 E86 .051 21.864 ( .066) Q58 =V87 = 1.089*F18 + 1.000 E87 .059 18.371 ( .078) Q59 =V88 = .927*F18 + 1.000 E88 .054 17.280 ( .068) Q12AB_CO=V89 = 1.000 F4 + 1.000 E89

146

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 14 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) VARIANCES OF INDEPENDENT VARIABLES

I .043 I

I .046 I

I .076 I

I .034 I

I .024 I

I .046 I

---------------------------------- V F --- --- I F4 - F4 .507*I I 11.706 I I ( .065)I I ( 7.824)I I I I F5 - F5 .250*I I 5.458 I I ( .050)I I ( 4.966)I I I I F10 - F10 .782*I I 10.253 I I ( .071)I I ( 10.985)I I I I F11 - F11 .276*I I 8.219 I I ( .047)I I ( 5.942)I I I I F17 - F17 .155*I I 6.373 I I ( .039)I I ( 3.963)I I I I F18 - F18 .483*I I 10.428 I I ( .060)I I ( 8.010)I I I

147

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.027 I I

.054 I I

.031 I I

.042 I I

.028 I I

.028 I I

.035 I I

.037 I I

.029 I I

.025 I I

13.650 I I ( .034)I I ( 10.165)I I I I E43 - Q31D .290*I I .022 I I 13.133 I I ( .025)I I ( 11.807)I I I I E44 - Q31E .379*I I .027 I I 14.133 I I ( .039)I I ( 9.621)I I I I

---------------------------------- E D --- --- E16 - Q12C .202*I I 7.625 I I ( .037)I I ( 5.532)I I I I E17 - Q13 .843*I I 15.481 I I ( .063)I I ( 13.476)I I I I E18 - Q14A .439*I I 14.224 I I ( .046)I I ( 9.595)I I I I E19 - Q14B .606*I I 14.290 I I ( .067)I I ( 9.117)I I I I E20 - Q14C .344*I I 12.292 I I ( .044)I I ( 7.859)I I I I E21 - Q14D .345*I I 12.507 I I ( .038)I I ( 9.029)I I I I E22 - Q14E .489*I I 14.109 I I ( .051)I I ( 9.618)I I I I E40 - Q31A .540*I I 14.646 I I ( .051)I I ( 10.638)I I I I E41 - Q31B .417*I I 14.194 I I ( .038)I I ( 10.936)I I I I E42 - Q31C .348*I I

148

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---------

I I

13.893 I I ( .039)I I ( 10.547)I I I I E52 - Q34 .418*I I .029 I I 14.566 I I ( .039)I I ( 10.865)I I I I E53 - Q35 .464*I I .032 I I 14.393 I I ( .060)I I ( 7.751)I I I I E54 - Q36 .386*I I .027 I I 14.200 I I ( .040)I I ( 9.649)I I I I E78 - Q49 .333*I I .023 I I 14.497 I I ( .030)I I ( 11.209)I I I I E79 - Q50 .316*I I .031 I I 10.107 I I ( .053)I I ( 5.929)I I I I

------------------------------------- E45 - Q31F .391*I .028 I 13.769 I I ( .033)I I ( 11.672)I I I I E46 - Q31G .446*I I .032 I I 14.051 I I ( .051)I I ( 8.766)I I I I E47 - Q32A .357*I I .024 I I 14.932 I I ( .036)I I ( 9.987)I I I I E48 - Q32B .316*I I .022 I I 14.309 I I ( .039)I I ( 8.171)I I I I E49 - Q32C .267*I I .018 I I 14.727 I I ( .025)I I ( 10.513)I I I I E50 - Q32D .309*I I .021 I I 14.394 I I ( .030)I I ( 10.280)I I I I E51 - Q33 .410*I I .029 I I

149

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---------

I I

------------------------------------- E80 - Q51 .364*I .032 I 11.225 I I ( .048)I I ( 7.538)I I I I E81 - Q52 .353*I I .023 I I 15.253 I I ( .048)I I ( 7.349)I I I I E82 - Q53 .312*I I .022 I I 14.360 I I ( .032)I I ( 9.865)I I I I E83 - Q54 .291*I I .020 I I 14.336 I I ( .028)I I ( 10.239)I I I I E84 - Q55 .353*I I .024 I I 14.583 I I ( .036)I I ( 9.923)I I I I E85 - Q56 .235*I I .018 I I 13.126 I I ( .034)I I ( 6.874)I I I I E86 - Q57 .208*I I .016 I I 12.772 I I ( .027)I I ( 7.712)I I I I E87 - Q58 .444*I I .030 I I 14.664 I I ( .045)I I ( 9.970)I I I I E88 - Q59 .400*I I .027 I I 14.962 I I ( .036)I I ( 11.027)I I I I E89 -Q12AB_CO .092*I I .024 I I 3.859 I I ( .031)I I ( 2.947)I I I I

150

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 18 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) COVARIANCES AMONG INDEPENDENT VARIABLES --------------------------------------- V F --- --- I F5 - F5 .209*I

I ( 8.440)I I I

I F4 - F4 .027 I I 7.760 I I ( .036)I I ( 5.847)I I I I F10 - F10 .264*I I F4 - F4 .034 I I 7.767 I I ( .041)I I ( 6.427)I I I I F11 - F11 .192*I I F4 - F4 .023 I I 8.492 I I ( .031)I I ( 6.137)I I I I F17 - F17 .105*I I F4 - F4 .017 I I 6.129 I I ( .026)I I ( 4.087)I I I I F18 - F18 .226*I I F4 - F4 .027 I I 8.292 I I ( .039)I I ( 5.838)I I I I F10 - F10 .223*I I F5 - F5 .031 I I 7.127 I I ( .036)I I ( 6.247)I I I I F11 - F11 .172*I I F5 - F5 .023 I I 7.588 I I ( .028)I I ( 6.037)I I I I F17 - F17 .093*I I F5 - F5 .015 I I 6.134 I I ( .020)I I ( 4.615)I I I I F18 - F18 .183*I I F5 - F5 .025 I I 7.285 I I ( .030)I I ( 6.103)I I I I F11 - F11 .289*I I F10 - F10 .031 I I 9.279 I I ( .036)I I ( 8.009)I I I I F17 - F17 .165*I I F10 - F10 .023 I I 7.156 I I ( .029)I I ( 5.659)I I I I F18 - F18 .329*I I F10 - F10 .036 I I 9.104 I I ( .039)I

151

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 19 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) COVARIANCES AMONG INDEPENDENT VARIABLES (CONTINUED)

-

S 05/24/03 PAGE : 20


R-SQUARED

.717 .229

.454 .445

.621 .608

.468 .591

.652 .704

.741 .658

.694 .667

.436 .537

.474 .526

.587 .500

.526 .551 .317 .617

.567 .424

.607 .611 .576 .721

.743 Q58 =V87 = .750*F18 + .661 E87 .563 Q59 =V88 = .713*F18 + .701 E88 .509 Q12AB_CO=V89 = .920 F4 + .392 E89 .846

-------------------------------------------------- I F17 - F17 .120*I I F11 - F11 .016 I I 7.625 I I ( .023)I I ( 5.205)I I I I F18 - F18 .240*I I F11 - F11 .025 I I 9.553 I I ( .033)I I ( 7.269)I I I I F18 - F18 .196*I I F17 - F17 .022 I I 8.785 I I ( .032)I I ( 6.132)I I I TITLE: Model created by EQS 5.7b -- 5F389I34.ED EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL STANDARDIZED SOLUTION: Q12C =V16 = .847*F4 + .532 E16 Q13 =V17 = .478 F5 + .878 E17 Q14A =V18 = .673*F5 + .739 E18 Q14B =V19 = .667*F5 + .745 E19 Q14C =V20 = .788*F5 + .616 E20 Q14D =V21 = .779*F5 + .626 E21 Q14E =V22 = .684*F5 + .730 E22 Q31A =V40 = .769 F10 + .639 E40 Q31B =V41 = .807*F10 + .590 E41 Q31C =V42 = .839*F10 + .544 E42 Q31D =V43 = .861*F10 + .509 E43 Q31E =V44 = .811*F10 + .584 E44 Q31F =V45 = .833*F10 + .553 E45 Q31G =V46 = .817*F10 + .577 E46 Q32A =V47 = .661 F11 + .751 E47 Q32B =V48 = .733*F11 + .680 E48 Q32C =V49 = .689*F11 + .725 E49 Q32D =V50 = .725*F11 + .689 E50 Q33 =V51 = .766*F11 + .643 E51 Q34 =V52 = .707*F11 + .707 E52 Q35 =V53 = .725*F11 + .689 E53 Q36 =V54 = .742*F11 + .670 E54 Q49 =V78 = .563 F17 + .826 E78 Q50 =V79 = .785*F17 + .619 E79 Q51 =V80 = .753*F17 + .658 E80 Q52 =V81 = .271*F17 + .429*F18 + .759 E81 Q53 =V82 = .779 F18 + .627 E82 Q54 =V83 = .781*F18 + .624 E83 Q55 =V84 = .759*F18 + .651 E84 Q56 =V85 = .849*F18 + .528 E85 Q57 =V86 = .862*F18 + .507 E86

152

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 21 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) CORRELATIONS AMONG INDEPENDENT VARIABLES --------------------------------------- V F --- --- I F5 - F5 .587*I I F4 - F4 I I I I F10 - F10 .419*I

------------

---------

S 05/24/03 PAGE : 22

AL DISTRIBUTION THEORY)

S WALD TEST US PROCESS

IVARIATE INCREMENT ------

ABILITY CHI-SQUARE PROBABILITY --

PPED IN THIS PROCESS.

I F4 - F4 I I I I F11 - F11 .512*I I F4 - F4 I I I I F17 - F17 .374*I I F4 - F4 I I I I F18 - F18 .456*I I F4 - F4 I I I I F10 - F10 .504*I I F5 - F5 I I I I F11 - F11 .653*I I F5 - F5 I I I I F17 - F17 .472*I I F5 - F5 I I I I F18 - F18 .528*I I F5 - F5 I I I I F11 - F11 .621*I I F10 - F10 I I I I F17 - F17 .474*I I F10 - F10 I I I I F18 - F18 .536*I I F10 - F10 I I I I F17 - F17 .581*I I F11 - F11 I I I I F18 - F18 .656*I I F11 - F11 I I I I F18 - F18 .716*I I F17 - F17 I I I ------------------------------------------------------------------- E N D O F M E T H O D ---------------------------------------------------------------------- TITLE: Model created by EQS 5.7b -- 5F389I34.ED EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORM WALD TEST (FOR DROPPING PARAMETERS) ROBUST INFORMATION MATRIX USED IN THI MULTIVARIATE WALD TEST BY SIMULTANEO CUMULATIVE MULTIVARIATE STATISTICS UN ---------------------------------- -------------- STEP PARAMETER CHI-SQUARE D.F. PROB ---- ----------- ---------- ---- ----------- ---------- --------- ************ NONE OF THE FREE PARAMETERS IS DRO

153

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/24/03 PAGE : 23 EQS/EM386 Licensee: Mikhail Koulikov MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) LAGRANGE MULTIPLIER TEST REQUIRES 730028 WORDS OF MEMORY.

ROGRAM ALLOCATES 900000 WORDS.

PARAMETERS)

ICS:

TY PARAMETER CHANGE

07 5

.155

70 33 1

0.743 18 28.279 0.000 0.403

HE REST S OMTTED]

05/24/03 PAGE : 24 Licensee: Mikhail Koulikov

IMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

LTIVARIATE LAGRANGE MULTIPLIER TEST BY SIMULTANEOUS PROCESS IN STAGE 1

E ARE:

VARIATE INCREMENT

I-SQUARE PROBABILITY

23 E87,E48 597.249 23 0.000 14.183 0.000 24 E53,E43 609.705 24 0.000 12.456 0.000 25 V83,F17 621.986 25 0.000 12.281 0.000 26 E19,E18 633.884 26 0.000 11.898 0.001 27 E50,E47 645.729 27 0.000 11.845 0.001 28 E50,E48 659.462 28 0.000 13.734 0.000 29 E50,E49 672.788 29 0.000 13.326 0.000 30 E54,E19 683.923 30 0.000 11.135 0.001 31 E88,E44 694.364 31 0.000 10.441 0.001 32 V21,F11 704.525 32 0.000 10.161 0.001 33 V20,F11 716.636 33 0.000 12.111 0.001 34 E48,E17 726.982 34 0.000 10.345 0.001 35 E50,E17 739.441 35 0.000 12.459 0.000 36 E18,E16 749.463 36 0.000 10.022 0.002 37 E85,E16 759.308 37 0.000 9.845 0.002 38 V43,F18 768.379 38 0.000 9.071 0.003 39 E48,E40 776.975 39 0.000 8.596 0.003 40 E81,E43 785.489 40 0.000 8.514 0.004 41 E81,E40 795.056 41 0.000 9.567 0.002 42 E46,E17 803.269 42 0.000 8.213 0.004 43 E87,E20 811.442 43 0.000 8.173 0.004 44 E84,E44 819.521 44 0.000 8.079 0.004

P LAGRANGE MULTIPLIER TEST (FOR ADDING ORDERED UNIVARIATE TEST STATIST NO CODE PARAMETER CHI-SQUARE PROBABILI -- ---- --------- ---------- ----------- ---------------- 1 2 6 E86,E85 64.983 0.000 0.1 2 2 6 E54,E53 58.956 0.000 0.16 3 2 6 E52,E51 53.594 0.000 0 4 2 12 V17,F10 41.197 0.000 0.372 5 2 6 E48,E47 37.858 0.000 0.102 6 2 12 V17,F11 31.588 0.000 0.6 7 2 6 E41,E40 30.957 0.000 0.1 8 2 6 E49,E47 29.785 0.000 0.08 9 2 12 V17,F17 28.608 0.000 10 2 12 V17,F[T TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 MAX MU PARAMETER SETS (SUBMATRICES) ACTIVE AT THIS STAG PVV PFV PFF PEE PDD GVV GVF GFV GFF BVF BFF CUMULATIVE MULTIVARIATE STATISTICS UNI ---------------------------------- -------------------- STEP PARAMETER CHI-SQUARE D.F. PROBABILITY CH ---- ----------- ---------- ---- ----------- ---------- ----------- 1 E86,E85 64.983 1 0.000 64.983 0.000 2 E54,E53 123.938 2 0.000 58.956 0.000 3 E52,E51 171.728 3 0.000 47.789 0.000 4 V17,F10 212.924 4 0.000 41.197 0.000 5 E48,E47 244.201 5 0.000 31.277 0.000 6 E49,E47 277.118 6 0.000 32.917 0.000 7 E41,E40 308.075 7 0.000 30.957 0.000 8 E87,E53 335.799 8 0.000 27.724 0.000 9 E49,E48 359.008 9 0.000 23.209 0.000 10 E49,E18 380.192 10 0.000 21.184 0.000 11 E87,E47 399.568 11 0.000 19.376 0.000 12 E78,E41 418.581 12 0.000 19.014 0.000 13 E53,E19 437.206 13 0.000 18.625 0.000 14 E52,E50 455.565 14 0.000 18.359 0.000 15 E53,E51 474.898 15 0.000 19.333 0.000 16 E85,E84 492.282 16 0.000 17.384 0.000 17 E44,E42 508.503 17 0.000 16.221 0.000 18 V42,F17 524.437 18 0.000 15.934 0.000 19 V87,F10 539.523 19 0.000 15.087 0.000 20 E80,E21 554.362 20 0.000 14.839 0.000 21 E52,E17 568.699 21 0.000 14.337 0.000 22 V17,F4 583.066 22 0.000 14.367 0.000

154

45 E87,E78 827.546 45 0.000 8.025 0.005 46 E88,E83 835.576 46 0.000 8.030 0.005 47 E46,E43 843.515 47 0.000 7.940 0.005

17 861.041 49 0.000 9.835 0.002 6

0.000 7.410 0.006 4 883.298 52 0.000 7.358 0.007

0.000 7.299 0.007 0.000 7.073 0.008

0.008 0.009

772 0.009 0.010

0.011 463 0.011

0.014 0.014

949 0.015 0.008

0.004 239 0.012

0.015 0.016

767 0.016 0.010

7 0.017 186 0.013

0.003 8 0.016

080 0.008 0.018

3 0.020 387 0.020

0.022 1 0.022

194 0.023 0.026

0.027 763 0.029

0.020 0.030

579 0.032 0.034

0.034 403 0.036

0.037 0.037

076 0.043 0.036

0.044 229 0.040

9.22 seconds

48 V84,F17 851.206 48 0.000 7.691 0.006 49 V87,F 50 E81,E18 868.530 50 0.000 7.488 0.00 51 E83,E50 875.940 51 52 V78,F 53 E89,E52 890.597 53 54 E86,E47 897.670 54 55 V17,F17 904.689 55 0.000 7.020 56 E45,E18 911.589 56 0.000 6.900 57 E48,E44 918.361 57 0.000 6. 58 E80,E46 925.043 58 0.000 6.683 59 E87,E41 931.558 59 0.000 6.514 60 E49,E19 938.020 60 0.000 6. 61 E89,E44 944.088 61 0.000 6.067 62 E43,E21 950.068 62 0.000 5.980 63 V53,F18 956.017 63 0.000 5. 64 E52,E47 962.967 64 0.000 6.951 65 V54,F18 971.080 65 0.000 8.112 66 E54,E18 977.318 66 0.000 6. 67 E54,E40 983.272 67 0.000 5.954 68 E82,E18 989.103 68 0.000 5.831 69 E85,E46 994.870 69 0.000 5. 70 E85,E44 1001.557 70 0.000 6.687 71 V79,F18 1007.214 71 0.000 5.65 72 E82,E79 1013.400 72 0.000 6. 73 E86,E79 1022.302 73 0.000 8.902 74 V86,F10 1028.121 74 0.000 5.81 75 E86,E83 1035.200 75 0.000 7. 76 E40,E22 1040.842 76 0.000 5.642 77 E43,E41 1046.265 77 0.000 5.42 78 E88,E50 1051.652 78 0.000 5. 79 E78,E44 1056.936 79 0.000 5.284 80 E88,E16 1062.197 80 0.000 5.26 81 E53,E40 1067.391 81 0.000 5. 82 V41,F5 1072.358 82 0.000 4.967 83 E50,E22 1077.237 83 0.000 4.879 84 E89,E19 1081.999 84 0.000 4. 85 V89,F10 1087.447 85 0.000 5.448 86 E48,E41 1092.129 86 0.000 4.683 87 V81,F4 1096.709 87 0.000 4. 88 E83,E80 1101.184 88 0.000 4.475 89 E52,E19 1105.659 89 0.000 4.475 90 E78,E50 1110.062 90 0.000 4. 91 E87,E17 1114.427 91 0.000 4.365 92 V52,F10 1118.777 92 0.000 4.350 93 E47,E16 1122.853 93 0.000 4. 94 E89,E53 1127.259 94 0.000 4.406 95 E40,E18 1131.321 95 0.000 4.062 96 E40,E17 1135.550 96 0.000 4.1 Execution begins at 23:33:22.27 Execution ends at 23:33:31.49 Elapsed time =

155

Appendix J: EQS output � Initial multigroup model run

the EQS copyright holder Peter M. Bentler.

, A STRUCTURAL EQUATION PROGRAM MULTIVARIATE SOFTWARE, INC. 85 - 1998.

ROGRAM CONTROL INFORMATION

ated by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS

12B; 0=Q14C;

4E; V23=Q15; V24=Q16; V25=Q17; 19; V28=Q20; V29=Q21; V30=Q22; 24; V33=Q25; V34=Q26; V35=Q27A;

V44=Q31E; V45=Q31F;

34; V53=Q35; V54=Q36; V55=Q37A; B; V57=Q38; V58=Q39; V59=Q40; V60=Q41;

Q43F; V68=Q43G; V69=Q43H; V70=Q44;

B; V77=Q48; V78=Q49; V79=Q50; V80=Q51;

58; V88=Q59; V89=Q12AB_CO; ION

389H34.EDS 05/25/03 PAGE : 2

76 E40= .531* ;

The EQS code is printed with permission of EQS COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) 19 P 1 /TITLE 2 Model cre 3 /SPECIFICATIONS 4 DATA='C:\THESIS\EN_IMP~1\T_EIMP2.ESS'; 5 VARIABLES= 89; CASES= 459; 6 METHODS=ML; GROUPS=2; 7 MATRIX=RAW; 8 /LABELS 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V2 13 V21=Q14D; V22=Q1 14 V26=Q18; V27=Q 15 V31=Q23; V32=Q 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 17 V41=Q31B; V42=Q31C; V43=Q31D; 18 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50=Q32D; 19 V51=Q33; V52=Q 20 V56=Q37 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43D; 22 V66=Q43E; V67= 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 24 V76=Q47 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q 27 /EQUAT 28 V16 = 1.160*F4 + 1.000 E16 ; 29 V17 = 1.000 F5 + 1.000 E17 ; 30 V18 = 1.042*F5 + 1.000 E18 ; 31 V19 = 1.064*F5 + 1.000 E19 ; 32 V20 = 1.007*F5 + 1.000 E20 ; 33 V21 = 1.158*F5 + 1.000 E21 ; 34 V22 = 1.097*F5 + 1.000 E22 ; 35 V40 = 1.000 F10 + 1.000 E40 ; 36 V41 = 1.030*F10 + 1.000 E41 ; 37 V42 = .998*F10 + 1.000 E42 ; 38 V43 = 1.079*F10 + 1.000 E43 ; 39 V44 = .928*F10 + 1.000 E44 ; 40 V45 = 1.044*F10 + 1.000 E45 ; 41 V46 = 1.122*F10 + 1.000 E46 ; 42 V47 = 1.000 F11 + 1.000 E47 ; 43 V48 = 1.890*F11 + 1.000 E48 ; 44 V49 = 2.031*F11 + 1.000 E49 ; 45 V50 = 2.106*F11 + 1.000 E50 ; 46 V51 = 2.734*F11 + 1.000 E51 ; 47 V52 = 2.470*F11 + 1.000 E52 ; 48 V53 = 2.935*F11 + 1.000 E53 ; 49 V54 = 2.809*F11 + 1.000 E54 ; 50 V78 = 1.000 F17 + 1.000 E78 ; 51 V79 = 1.696*F17 + 1.000 E79 ; 52 V80 = 1.793*F17 + 1.000 E80 ; TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F EQS/EM386 Licensee: Mikhail Koulikov 53 V81 = .886*F17 + .572*F18 + 1.000 E81 ; 54 V82 = 1.000 F18 + 1.000 E82 ; 55 V83 = .884*F18 + 1.000 E83 ; 56 V84 = 1.047*F18 + 1.000 E84 ; 57 V85 = .987*F18 + 1.000 E85 ; 58 V86 = 1.061*F18 + 1.000 E86 ; 59 V87 = 1.205*F18 + 1.000 E87 ; 60 V88 = .817*F18 + 1.000 E88 ; 61 V89 = 1.000 F4 + 1.000 E89 ; 62 /VARIANCES 63 F4= .432* ; 64 F5= .369* ; 65 F10= .852* ; 66 F11= .090* ; 67 F17= .225* ; 68 F18= .679* ; 69 E16= .085* ; 70 E17= .771* ; 71 E18= .791* ; 72 E19= .617* ; 73 E20= .366* ; 74 E21= .303* ; 75 E22= .463* ;

156

77 E41= .364* ; 78 E42= .412* ; 79 E43= .348* ; 80 E44= .379* ; 81 E45= .457* ; 82 E46= .295* ; 83 E47= .498* ; 84 E48= .374* ; 85 E49= .347* ; 86 E50= .335* ; 87 E51= .471* ; 88 E52= .462* ; 89 E53= .515* ; 90 E54= .349* ; 91 E78= .355* ; 92 E79= .238* ; 93 E80= .382* ; 94 E81= .287* ; 95 E82= .276* ; 96 E83= .468* ; 97 E84= .456* ; 98 E85= .270* ; 99 E86= .167* ; 100 E87= .295* ; 101 E88= .333* ; 102 E89= .104* ; 103 /COVARIANCES 104 F5,F4 = .196* ; 105 F10,F4 = .140* ; 106 F10,F5 = .229* ; 107 F11,F4 = .083* ; 108 F11,F5 = .134* ; 109 F11,F10 = .138* ; TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H34.EDS 05/25/03 PAGE : 3

RE READ (GROUP 1)

05/25/03 PAGE : 4

MATION

20 /TITLE 21 Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389H34.EDS

122 /SPECIFICATIONS 123 DATA='C:\THESIS\SP_IMP~1\T_SIMP2.ESS'; 124 VARIABLES= 89; CASES= 524; 125 METHODS=ML; 126 MATRIX=RAW; 127 /LABELS 128 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 129 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 130 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q12B; 131 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V20=Q14C; 132 V21=Q14D; V22=Q14E; V23=Q15; V24=Q16; V25=Q17; 133 V26=Q18; V27=Q19; V28=Q20; V29=Q21; V30=Q22; 134 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 135 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 136 V41=Q31B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F; 137 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50=Q32D; 138 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 139 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V60=Q41; 140 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43D; 141 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 142 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 143 V76=Q47B; V77=Q48; V78=Q49; V79=Q50; V80=Q51; 144 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 145 V86=Q57; V87=Q58; V88=Q59; V89=Q12AB_CO; 146 /EQUATION 147 V16 = 1.006*F4 + 1.000 E16 ; 148 V17 = 1.000 F5 + 1.000 E17 ; 149 V18 = 1.207*F5 + 1.000 E18 ; 150 V19 = 1.394*F5 + 1.000 E19 ; 151 V20 = 1.500*F5 + 1.000 E20 ; 152 V21 = 1.460*F5 + 1.000 E21 ; 153 V22 = 1.310*F5 + 1.000 E22 ; 154 V40 = 1.000 F10 + 1.000 E40 ; 155 V41 = 1.000*F10 + 1.000 E41 ; 156 V42 = 1.029*F10 + 1.000 E42 ;

EQS/EM386 Licensee: Mikhail Koulikov 110 F17,F4 = .089* ; 111 F17,F5 = .132* ; 112 F17,F10 = .229* ; 113 F17,F11 = .069* ; 114 F18,F4 = .236* ; 115 F18,F5 = .344* ; 116 F18,F10 = .401* ; 117 F18,F11 = .197* ; 118 F18,F17 = .230* ; 119 /END 119 CUMULATED RECORDS OF INPUT MODEL FILE WE TITLE: EQS/EM386 Licensee: Mikhail Koulikov PROGRAM CONTROL INFOR 1 1

157

157 V43 = 1.030*F10 + 1.000 E43 ; 158 V44 = .967*F10 + 1.000 E44 ; 159 V45 = 1.065*F10 + 1.000 E45 160 V46 = 1.070*F10 + 1.000 E46 ; 161 V47 = 1.000 F11 + 1.000 E47 ;

64 V50 = 1.113*F11 + 1.000 E50 ; 65 V51 = 1.450*F11 + 1.000 E51 ;

05/25/03 PAGE : 5

221 E89= .092* ;

1\5F389H34.EDS 05/25/03 PAGE : 6

;

162 V48 = 1.152*F11 + 1.000 E48 ; 163 V49 = .933*F11 + 1.000 E49 ; 1 1 166 V52 = 1.231*F11 + 1.000 E52 ; 167 V53 = 1.364*F11 + 1.000 E53 ; 168 V54 = 1.310*F11 + 1.000 E54 ; 169 V78 = 1.000 F17 + 1.000 E78 ; 170 V79 = 1.812*F17 + 1.000 E79 ; 171 V80 = 1.756*F17 + 1.000 E80 ; 172 V81 = .540*F17 + .483*F18 + 1.000 E81 ; 173 V82 = 1.000 F18 + 1.000 E82 ; 174 V83 = .972*F18 + 1.000 E83 ; 175 V84 = .997*F18 + 1.000 E84 ; 176 V85 = 1.124*F18 + 1.000 E85 ; 177 V86 = 1.115*F18 + 1.000 E86 ; 178 V87 = 1.089*F18 + 1.000 E87 ; 179 V88 = .927*F18 + 1.000 E88 ; TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389H34.EDS EQS/EM386 Licensee: Mikhail Koulikov 180 V89 = 1.000 F4 + 1.000 E89 ; 181 /VARIANCES 182 F4= .507* ; 183 F5= .250* ; 184 F10= .782* ; 185 F11= .277* ; 186 F17= .155* ; 187 F18= .483* ; 188 E16= .202* ; 189 E17= .843* ; 190 E18= .438* ; 191 E19= .606* ; 192 E20= .344* ; 193 E21= .345* ; 194 E22= .489* ; 195 E40= .540* ; 196 E41= .417* ; 197 E42= .348* ; 198 E43= .290* ; 199 E44= .379* ; 200 E45= .391* ; 201 E46= .446* ; 202 E47= .357* ; 203 E48= .316* ; 204 E49= .267* ; 205 E50= .309* ; 206 E51= .410* ; 207 E52= .419* ; 208 E53= .464* ; 209 E54= .386* ; 210 E78= .333* ; 211 E79= .316* ; 212 E80= .364* ; 213 E81= .353* ; 214 E82= .312* ; 215 E83= .291* ; 216 E84= .353* ; 217 E85= .235* ; 218 E86= .208* ; 219 E87= .444* ; 220 E88= .400* ; 222 /COVARIANCES 223 F5,F4 = .209* ; 224 F10,F4 = .264* ; 225 F10,F5 = .223* ; 226 F11,F4 = .192* ; 227 F11,F5 = .172* ; 228 F11,F10 = .289* ; 229 F17,F4 = .105* ; 230 F17,F5 = .093* ; 231 F17,F10 = .165* ; 232 F17,F11 = .120* ; 233 F18,F4 = .226* ; 234 F18,F5 = .184* ; 235 F18,F10 = .329* ; 236 F18,F11 = .240* ; TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~ EQS/EM386 Licensee: Mikhail Koulikov 237 F18,F17 = .196* ; 238 /CONSTRAINTS 239 (1,F4,F4)=(2,F4,F4); 240 (1,F5,F5)=(2,F5,F5); 241 (1,F10,F10)=(2,F10,F10); 242 (1,F11,F11)=(2,F11,F11); 243 (1,F17,F17)=(2,F17,F17);

158

244 (1,F18,F18)=(2,F18,F18);

E41,E41);

E54,E54); E78,E78);

E80,E80);

THESIS\SP_IMP~1\5F389H34.EDS 05/25/03 PAGE : 7 QS/EM386 Licensee: Mikhail Koulikov

294 (1,E81,E81)=(2,E81,E81);

296 (1,E83,E83)=(2,E83,E83);

2)

245 (1,F4,F5)=(2,F4,F5); 246 (1,V16,F4)=(2,V16,F4); 247 (1,V18,F5)=(2,V18,F5); 248 (1,V19,F5)=(2,V19,F5); 249 (1,V20,F5)=(2,V20,F5); 250 (1,V21,F5)=(2,V21,F5); 251 (1,V22,F5)=(2,V22,F5); 252 (1,V41,F10)=(2,V41,F10); 253 (1,V42,F10)=(2,V42,F10); 254 (1,V43,F10)=(2,V43,F10); 255 (1,V44,F10)=(2,V44,F10); 256 (1,V45,F10)=(2,V45,F10); 257 (1,V46,F10)=(2,V46,F10); 258 (1,V48,F11)=(2,V48,F11); 259 (1,V49,F11)=(2,V49,F11); 260 (1,V50,F11)=(2,V50,F11); 261 (1,V52,F11)=(2,V52,F11); 262 (1,V53,F11)=(2,V53,F11); 263 (1,V54,F11)=(2,V54,F11); 264 (1,V79,F17)=(2,V79,F17); 265 (1,V80,F17)=(2,V80,F17); 266 (1,V81,F17)=(2,V81,F17); 267 (1,V81,F18)=(2,V81,F18); 268 (1,E89,E89)=(2,E89,E89); 269 (1,E16,E16)=(2,E16,E16); 270 (1,E17,E17)=(2,E17,E17); 271 (1,E18,E18)=(2,E18,E18); 272 (1,E19,E19)=(2,E19,E19); 273 (1,E20,E20)=(2,E20,E20); 274 (1,E21,E21)=(2,E21,E21); 275 (1,E22,E22)=(2,E22,E22); 276 (1,E40,E40)=(2,E40,E40); 277 (1,E41,E41)=(2, 278 (1,E42,E42)=(2,E42,E42); 279 (1,E43,E43)=(2,E43,E43); 280 (1,E44,E44)=(2,E44,E44); 281 (1,E45,E45)=(2,E45,E45); 282 (1,E46,E46)=(2,E46,E46); 283 (1,E47,E47)=(2,E47,E47); 284 (1,E48,E48)=(2,E48,E48); 285 (1,E49,E49)=(2,E49,E49); 286 (1,E50,E50)=(2,E50,E50); 287 (1,E51,E51)=(2,E51,E51); 288 (1,E52,E52)=(2,E52,E52); 289 (1,E53,E53)=(2,E53,E53); 290 (1,E54,E54)=(2, 291 (1,E78,E78)=(2, 292 (1,E79,E79)=(2,E79,E79); 293 (1,E80,E80)=(2, TITLE: Model created by EQS 5.7b -- C:\ E 295 (1,E82,E82)=(2,E82,E82); 297 (1,E84,E84)=(2,E84,E84); 298 (1,E85,E85)=(2,E85,E85); 299 (1,E86,E86)=(2,E86,E86); 300 (1,E87,E87)=(2,E87,E87); 301 (1,E88,E88)=(2,E88,E88); 302 (1,V83,F18)=(2,V83,F18); 303 (1,V84,F18)=(2,V84,F18); 304 (1,V85,F18)=(2,V85,F18); 305 (1,V86,F18)=(2,V86,F18); 306 (1,V87,F18)=(2,V87,F18); 307 (1,V88,F18)=(2,V88,F18); 308 /LMTEST 309 /END 309 CUMULATED RECORDS OF INPUT MODEL FILE WERE READ (GROUP DATA IS READ FROM C:\THESIS\EN_IMP~1\T_EIMP2.ESS THERE ARE 89 VARIABLES AND 459 CASES IT IS A RAW DATA ESS FILE

159


4

1

IS (G2) -0.3384 0.0126 -0.6201 -0.5117 2.0641

0.4300 0.1731 1.0065

6 1.0844 .8864 1.0511

3 0.7610 .5959 0.9650

S

IVARIATE KURTOSIS = 0.1433 0.2889

NORMALIZED MULTIVARIATE KURTOSIS:

51 904 1740.4422

34.EDS 05/25/03 PAGE : 9

LES (SELECTED FROM 89 VARIABLES) BASED ON 459 CASES. [OMTTED]

41 42 51 52 4 85

41 42 INDEPENDENT E'S : 43 44 45 46 47 48 49 50 51 52

89

EQS/EM386 Licensee: Mikhail Koulikov SAMPLE STATISTICS BASED ON COMPLETE CASES UNIVARIATE STATISTICS --------------------- VARIABLE Q12C Q13 Q14A Q14B Q14C MEAN 2.0463 2.9955 2.7257 2.6569 2.5213 SKEWNESS (G1) 1.2849 0.2462 0.6930 0.7470 1.0043 KURTOSIS (G2) 2.7548 -0.5398 -0.3705 -0.0670 1.1043 STANDARD DEV. 0.8161 1.0676 1.0918 1.0172 0.860 VARIABLE Q14D Q14E Q31A Q31B Q31C MEAN 2.6796 2.6714 2.4633 2.4120 2.5485 SKEWNESS (G1) 0.7471 0.6618 0.6807 0.6057 0.5265 KURTOSIS (G2) 0.4712 0.2002 -0.2444 -0.3215 -0.3329 STANDARD DEV. 0.8929 0.9523 1.1762 1.1261 1.123 VARIABLE Q31D Q31E Q31F Q31G Q32A MEAN 2.4653 2.2348 2.6058 2.4699 2.2744 SKEWNESS (G1) 0.6306 0.7215 0.3958 0.5201 0.8855 KURTOS STANDARD DEV. 1.1571 1.0548 1.1777 1.1690 0.7673 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.4521 2.4240 2.3980 2.9835 2.7671 SKEWNESS (G1) 0.8656 0.9184 0.8323 0.3758 KURTOSIS (G2) 1.0557 1.2368 1.1149 -0.5042 - STANDARD DEV. 0.8343 0.8484 0.8579 1.0705 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.9610 2.6752 1.8093 2.0446 2.133 SKEWNESS (G1) 0.3007 0.5288 1.2328 1.0135 KURTOSIS (G2) -0.6359 0.0936 2.8132 1.0842 0 STANDARD DEV. 1.1372 1.0307 0.7615 0.9405 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3489 2.4360 2.5698 2.5774 2.287 SKEWNESS (G1) 0.7493 0.5879 0.7131 0.5796 KURTOSIS (G2) 0.7948 0.1410 0.2351 -0.1173 0 STANDARD DEV. 0.9588 0.9773 0.9994 1.0955 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.3955 2.6554 2.6039 1.9840 SKEWNESS (G1) 0.6048 0.4939 0.3295 1.0128 KURTOSIS (G2) 0.3116 -0.3478 0.3661 2.4074 STANDARD DEV. 0.9649 1.1314 0.8864 0.7320 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 353.5560 NORMALIZED ESTIMATE = 76.5471 ELLIPTICAL THEORY KURTOSIS ESTIMATE ------------------------------------ MARDIA-BASED KAPPA = 0.2889 MEAN SCALED UN MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA= CASE NUMBERS WITH LARGEST CONTRIBUTION TO --------------------------------------------------------------------------- CASE NUMBER 35 272 281 355 4 ESTIMATE 2345.8423 1590.5983 1690.5610 1755.8 TITLE: Model created by EQS 5.7b -- C:\THESIS\EN_IMP~1\5F389H EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 34 VARIAB BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V'S : 16 17 18 19 20 21 22 40 DEPENDENT V'S : 43 44 45 46 47 48 49 50 DEPENDENT V'S : 53 54 78 79 80 81 82 83 8 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 40 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 40 INDEPENDENT E'S : 53 54 78 79 80 81 82 83 84 85 INDEPENDENT E'S : 86 87 88 NUMBER OF FREE PARAMETERS = 84 NUMBER OF FIXED NONZERO PARAMETERS = 40 DATA IS READ FROM C:\THESIS\SP_IMP~1\T_SIMP2.ESS THERE ARE 89 VARIABLES AND 524 CASES IT IS A RAW DATA ESS FILE

160

TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389H34.EDS 05/25/03 PAGE : 10

23

45

59

9 52

6 73

8 92

---------------------

139

IATE KURTOSIS = 0.3548 = 0.4507

LIZED MULTIVARIATE KURTOSIS:

3803.2697

9H34.EDS 05/25/03 PAGE : 11

ELECTED FROM 89 VARIABLES) [OMTTED]

ENTLER-WEEKS STRUCTURAL REPRESENTATION:

DEPENDENT V'S : 16 17 18 19 20 21 22 40 41 42 DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 52

NUMBER OF INDEPENDENT VARIABLES = 40 INDEPENDENT F'S : 4 5 10 11 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 40 41 42 INDEPENDENT E'S : 43 44 45 46 47 48 49 50 51 52 INDEPENDENT E'S : 53 54 78 79 80 81 82 83 84 85 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 84 NUMBER OF FIXED NONZERO PARAMETERS = 40 3RD STAGE OF COMPUTATION REQUIRED 132200 WORDS OF MEMORY. PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IN GROUP 1 IS 0.43722E-11 DETERMINANT OF INPUT MATRIX IN GROUP 2 IS 0.15676E-11 MATRIX SIGMA_MX MAY NOT BE POSITIVE DEFINITE.

EQS/EM386 Licensee: Mikhail Koulikov SAMPLE STATISTICS BASED ON COMPLETE CASES UNIVARIATE STATISTICS --------------------- VARIABLE Q12C Q13 Q14A Q14B Q14C MEAN 2.1452 2.6965 2.2206 2.5646 2.4495 SKEWNESS (G1) 1.3891 0.5783 1.3119 0.8376 0.9837 KURTOSIS (G2) 2.7887 -0.1237 2.0481 0.1406 0.8440 STANDARD DEV. 0.8455 1.0454 0.8959 1.0453 0.95 VARIABLE Q14D Q14E Q31A Q31B Q31C MEAN 2.5451 2.4848 2.7802 2.8403 2.7775 SKEWNESS (G1) 0.9346 1.0151 0.4534 0.3549 0.4455 KURTOSIS (G2) 0.8134 0.9112 -0.5514 -0.5155 -0.3754 STANDARD DEV. 0.9372 0.9587 1.1498 1.0947 1.08 VARIABLE Q31D Q31E Q31F Q31G Q32A MEAN 2.5928 2.5787 2.7023 2.6083 2.1897 SKEWNESS (G1) 0.6602 0.6850 0.4831 0.5893 1.2281 KURTOSIS (G2) -0.0802 -0.0407 -0.4307 -0.3730 2.7548 STANDARD DEV. 1.0582 1.0532 1.1304 1.1576 0.79 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.2059 2.2134 2.2424 2.4903 2.4678 SKEWNESS (G1) 1.1591 1.0335 1.0235 0.9294 0.860 KURTOSIS (G2) 2.3899 2.7005 2.0292 0.7101 0.9357 STANDARD DEV. 0.8262 0.7121 0.8069 0.9954 0.91 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.5802 2.3638 1.8900 2.3733 2.3009 SKEWNESS (G1) 0.7756 0.9311 1.2237 0.9512 0.944 KURTOSIS (G2) 0.4536 1.1508 3.4898 0.9744 0.8905 STANDARD DEV. 0.9894 0.9280 0.6985 0.9077 0.91 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3230 2.2813 2.3224 2.3833 2.2631 SKEWNESS (G1) 1.0258 1.0365 0.8822 0.8659 1.031 KURTOSIS (G2) 2.5850 1.4789 1.0254 1.0156 1.4527 STANDARD DEV. 0.7830 0.8915 0.8647 0.9125 0.91 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.2874 2.5839 2.4621 2.1368 SKEWNESS (G1) 0.9808 0.6071 0.6698 1.2458 KURTOSIS (G2) 1.3401 0.0862 1.0939 2.5796 STANDARD DEV. 0.8986 1.0086 0.9026 0.7738 MULTIVARIATE KURTOSIS MARDIA'S COEFFICIENT (G2,P) = 551.6557 NORMALIZED ESTIMATE = 127.6 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.4507 MEAN SCALED UNIVAR MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORMA --------------------------------------------------------------------------- CASE NUMBER 147 206 235 510 514 ESTIMATE 2968.2113 2661.4384 3206.6996 3151.3172 TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F38 EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 34 VARIABLES (S BASED ON 524 CASES. B NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V'S : 53 54 78 79 80 81 82 83 84 85 DEPENDENT V'S : 86 87 88 89

161

Appendix K: EQS output � Final multigroup model run

with permission of the EQS copyright holder Peter M. Bentler.

VARIATE SOFTWARE, INC. 85 - 1998.

ated by EQS 5.7b -- 5F389I34.EDS

LABELS

19; V28=Q20; V29=Q21; V30=Q22;

B; V57=Q38; V58=Q39; V59=Q40; V60=Q41;

D; 4;

58; V88=Q59; V89=Q12AB_CO;

10 + 1.000 E44 ;

E46 ; 11 + 1.000 E47 ;

17 + 1.000 E78 ;

05/11/03 PAGE : 2

17 + .571*F18 + 1.000 E81 ; 18 + 1.000 E82 ;

18 + 1.000 E88 ;

11= .135* ;

80 E43= .348* ;

The EQS code is printed EQS, A STRUCTURAL EQUATION PROGRAM MULTI COPYRIGHT BY P.M. BENTLER VERSION 5.7b (C) 19 PROGRAM CONTROL INFORMATION 1 /TITLE 2 Model cre 3 /SPECIFICATIONS 4 DATA='C:\THESIS\EN_IMP~1\T_EIMP2.ESS'; 5 VARIABLES= 89; CASES= 459; 6 METHODS=ML; GROUPS=2; 7 MATRIX=RAW; 8 / 9 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 10 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 11 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q12B; 12 V16=Q12C; V17=Q13; V18=Q14A; V19=Q14B; V20=Q14C; 13 V21=Q14D; V22=Q14E; V23=Q15; V24=Q16; V25=Q17; 14 V26=Q18; V27=Q 15 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 16 V36=Q27B; V37=Q28; V38=Q29; V39=Q30; V40=Q31A; 17 V41=Q31B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F; 18 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50=Q32D; 19 V51=Q33; V52=Q34; V53=Q35; V54=Q36; V55=Q37A; 20 V56=Q37 21 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43 22 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q4 23 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 24 V76=Q47B; V77=Q48; V78=Q49; V79=Q50; V80=Q51; 25 V81=Q52; V82=Q53; V83=Q54; V84=Q55; V85=Q56; 26 V86=Q57; V87=Q 27 /EQUATION 28 V16 = 1.156*F4 + 1.000 E16 ; 29 V17 = 1.000 F5 + 1.000 E17 ; 30 V18 = 1.042*F5 + 1.000 E18 ; 31 V19 = 1.064*F5 + 1.000 E19 ; 32 V20 = 1.008*F5 + 1.000 E20 ; 33 V21 = 1.158*F5 + 1.000 E21 ; 34 V22 = 1.097*F5 + 1.000 E22 ; 35 V40 = 1.000 F10 + 1.000 E40 ; 36 V41 = 1.030*F10 + 1.000 E41 ; 37 V42 = .999*F10 + 1.000 E42 ; 38 V43 = 1.078*F10 + 1.000 E43 ; 39 V44 = .928*F 40 V45 = 1.044*F10 + 1.000 E45 ; 41 V46 = 1.121*F10 + 1.000 42 V47 = 1.000 F 43 V48 = 1.806*F11 + 1.000 E48 ; 44 V49 = 1.829*F11 + 1.000 E49 ; 45 V50 = 1.909*F11 + 1.000 E50 ; 46 V51 = 1.000 F12 + 1.000 E51 ; 47 V52 = .888*F12 + 1.000 E52 ; 48 V53 = 1.104*F12 + 1.000 E53 ; 49 V54 = 1.033*F12 + 1.000 E54 ; 50 V78 = 1.000 F 51 V79 = 1.689*F17 + 1.000 E79 ; 52 V80 = 1.788*F17 + 1.000 E80 ; TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov 53 V81 = .885*F 54 V82 = 1.000 F 55 V83 = .884*F18 + 1.000 E83 ; 56 V84 = 1.044*F18 + 1.000 E84 ; 57 V85 = .985*F18 + 1.000 E85 ; 58 V86 = 1.059*F18 + 1.000 E86 ; 59 V87 = 1.204*F18 + 1.000 E87 ; 60 V88 = .815*F 61 V89 = 1.000 F4 + 1.000 E89 ; 62 /VARIANCES 63 F4= .434* ; 64 F5= .369* ; 65 F10= .852* ; 66 F 67 F12= .707* ; 68 F17= .226* ; 69 F18= .680* ; 70 E16= .087* ; 71 E17= .771* ; 72 E18= .792* ; 73 E19= .617* ; 74 E20= .366* ; 75 E21= .303* ; 76 E22= .463* ; 77 E40= .531* ; 78 E41= .364* ; 79 E42= .412* ;

162

81 E44= .379* ; 82 E45= .458* ;

5,F4 = .197* ;


17,F4 = .089* ;

18,F5 = .344* ;

LATED RECORDS OF INPUT MODEL FILE WERE READ (GROUP 1) 05/11/03 PAGE : 4

ulikov

ated by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS

MP2.ESS';

IX=RAW;

18=Q14A; V19=Q14B; V20=Q14C;

=Q21; V30=Q22;

V38=Q29; V39=Q30; V40=Q31A; B; V42=Q31C; V43=Q31D; V44=Q31E; V45=Q31F;

=Q32D; Q34; V53=Q35; V54=Q36; V55=Q37A;

0=Q41;

Q50; V80=Q51; Q53; V83=Q54; V84=Q55; V85=Q56;

167 V46 = 1.069*F10 + 1.000 E46 ;

83 E46= .295* ; 84 E47= .453* ; 85 E48= .255* ; 86 E49= .267* ; 87 E50= .243* ; 88 E51= .439* ; 89 E52= .456* ; 90 E53= .432* ; 91 E54= .308* ; 92 E78= .354* ; 93 E79= .240* ; 94 E80= .382* ; 95 E81= .287* ; 96 E82= .275* ; 97 E83= .467* ; 98 E84= .458* ; 99 E85= .272* ; 100 E86= .168* ; 101 E87= .293* ; 102 E88= .333* ; 103 E89= .102* ; 104 /COVARIANCES 105 F 106 F10,F4 = .140* ; 107 F10,F5 = .229* ; 108 F11,F4 = .092* ; 109 F11,F5 = .146* ; TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 110 F11,F10 = .143* ; 111 F12,F4 = .222* ; 112 F12,F5 = .363* ; 113 F12,F10 = .384* ; 114 F12,F11 = .241* ; 115 F 116 F17,F5 = .133* ; 117 F17,F10 = .229* ; 118 F17,F11 = .077* ; 119 F17,F12 = .185* ; 120 F18,F4 = .237* ; 121 F 122 F18,F10 = .401* ; 123 F18,F11 = .201* ; 124 F18,F12 = .557* ; 125 F18,F17 = .231* ; 126 /END 126 CUMU TITLE: EQS/EM386 Licensee: Mikhail Ko PROGRAM CONTROL INFORMATION 127 /TITLE 128 Model cre 129 /SPECIFICATIONS 130 DATA='C:\THESIS\SP_IMP~1\T_SI 131 VARIABLES= 89; CASES= 524; 132 METHODS=ML; 133 MATR 134 /LABELS 135 V1=ID; V2=Q1; V3=Q2; V4=Q3; V5=Q4; 136 V6=Q5; V7=Q6A; V8=Q6B; V9=Q7; V10=Q8; 137 V11=Q9; V12=Q10; V13=Q11; V14=Q12A; V15=Q12B; 138 V16=Q12C; V17=Q13; V 139 V21=Q14D; V22=Q14E; V23=Q15; V24=Q16; V25=Q17; 140 V26=Q18; V27=Q19; V28=Q20; V29 141 V31=Q23; V32=Q24; V33=Q25; V34=Q26; V35=Q27A; 142 V36=Q27B; V37=Q28; 143 V41=Q31 144 V46=Q31G; V47=Q32A; V48=Q32B; V49=Q32C; V50 145 V51=Q33; V52= 146 V56=Q37B; V57=Q38; V58=Q39; V59=Q40; V6 147 V61=Q42; V62=Q43A; V63=Q43B; V64=Q43C; V65=Q43D; 148 V66=Q43E; V67=Q43F; V68=Q43G; V69=Q43H; V70=Q44; 149 V71=Q45; V72=Q46A; V73=Q46B; V74=Q46C; V75=Q47A; 150 V76=Q47B; V77=Q48; V78=Q49; V79= 151 V81=Q52; V82= 152 V86=Q57; V87=Q58; V88=Q59; V89=Q12AB_CO; 153 /EQUATION 154 V16 = 1.006*F4 + 1.000 E16 ; 155 V17 = 1.000 F5 + 1.000 E17 ; 156 V18 = 1.209*F5 + 1.000 E18 ; 157 V19 = 1.396*F5 + 1.000 E19 ; 158 V20 = 1.503*F5 + 1.000 E20 ; 159 V21 = 1.463*F5 + 1.000 E21 ; 160 V22 = 1.313*F5 + 1.000 E22 ; 161 V40 = 1.000 F10 + 1.000 E40 ; 162 V41 = .999*F10 + 1.000 E41 ; 163 V42 = 1.028*F10 + 1.000 E42 ; 164 V43 = 1.029*F10 + 1.000 E43 ; 165 V44 = .966*F10 + 1.000 E44 ; 166 V45 = 1.065*F10 + 1.000 E45 ;

163

168 V47 = 1.000 F11 + 1.000 E47 ; 169 V48 = 1.123*F11 + 1.000 E48 170 V49 = .900*F11 + 1.000 E49 ; 171 V50 = 1.039*F11 + 1.000 E50 ; 172 V51 = 1.000 F12 + 1.000 E51 ;

75 V54 = .895*F12 + 1.000 E54 ; 76 V78 = 1.000 F17 + 1.000 E78 ;

00 E81 ;

1\5F389I34.EDS 05/11/03 PAGE : 5

1\5F389I34.EDS 05/11/03 PAGE : 6

;

173 V52 = .866*F12 + 1.000 E52 ; 174 V53 = .974*F12 + 1.000 E53 ; 1 1 177 V79 = 1.813*F17 + 1.000 E79 ; 178 V80 = 1.756*F17 + 1.000 E80 ; 179 V81 = .539*F17 + .484*F18 + 1.0 180 V82 = 1.000 F18 + 1.000 E82 ; 181 V83 = .972*F18 + 1.000 E83 ; 182 V84 = .996*F18 + 1.000 E84 ; 183 V85 = 1.123*F18 + 1.000 E85 ; 184 V86 = 1.114*F18 + 1.000 E86 ; 185 V87 = 1.092*F18 + 1.000 E87 ; 186 V88 = .927*F18 + 1.000 E88 ; TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~ EQS/EM386 Licensee: Mikhail Koulikov 187 V89 = 1.000 F4 + 1.000 E89 ; 188 /VARIANCES 189 F4= .507* ; 190 F5= .249* ; 191 F10= .783* ; 192 F11= .343* ; 193 F12= .616* ; 194 F17= .155* ; 195 F18= .483* ; 196 E16= .202* ; 197 E17= .844* ; 198 E18= .438* ; 199 E19= .607* ; 200 E20= .344* ; 201 E21= .344* ; 202 E22= .489* ; 203 E40= .539* ; 204 E41= .417* ; 205 E42= .348* ; 206 E43= .291* ; 207 E44= .379* ; 208 E45= .391* ; 209 E46= .446* ; 210 E47= .290* ; 211 E48= .250* ; 212 E49= .229* ; 213 E50= .280* ; 214 E51= .375* ; 215 E52= .376* ; 216 E53= .394* ; 217 E54= .367* ; 218 E78= .333* ; 219 E79= .316* ; 220 E80= .364* ; 221 E81= .353* ; 222 E82= .312* ; 223 E83= .291* ; 224 E84= .353* ; 225 E85= .236* ; 226 E86= .208* ; 227 E87= .442* ; 228 E88= .400* ; 229 E89= .092* ; 230 /COVARIANCES 231 F5,F4 = .208* ; 232 F10,F4 = .264* ; 233 F10,F5 = .223* ; 234 F11,F4 = .195* ; 235 F11,F5 = .177* ; 236 F11,F10 = .277* ; 237 F12,F4 = .279* ; 238 F12,F5 = .246* ; 239 F12,F10 = .439* ; 240 F12,F11 = .374* ; 241 F17,F4 = .105* ; 242 F17,F5 = .093* ; 243 F17,F10 = .165* ; TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~ EQS/EM386 Licensee: Mikhail Koulikov 244 F17,F11 = .119* ; 245 F17,F12 = .178* ; 246 F18,F4 = .226* ; 247 F18,F5 = .183* ; 248 F18,F10 = .329* ; 249 F18,F11 = .233* ; 250 F18,F12 = .361* ; 251 F18,F17 = .196* ; 252 /CONSTRAINTS 253 (1,F4,F4)=(2,F4,F4); 254 (1,F5,F5)=(2,F5,F5); 255 (1,F10,F10)=(2,F10,F10); 256 (1,F11,F11)=(2,F11,F11);

164

257 (1,F12,F12)=(2,F12,F12);

5F389I34.EDS 05/11/03 PAGE : 7

READ (GROUP 2) ATA ESS FILE

258 (1,F17,F17)=(2,F17,F17); 259 (1,F18,F18)=(2,F18,F18); 260 (1,F4,F5)=(2,F4,F5); 261 (1,F4,F10)=(2,F4,F10); 262 (1,F4,F11)=(2,F4,F11); 263 (1,F4,F12)=(2,F4,F12); 264 (1,F4,F17)=(2,F4,F17); 265 (1,F4,F18)=(2,F4,F18); 266 (1,F5,F10)=(2,F5,F10); 267 (1,F5,F11)=(2,F5,F11); 268 (1,F5,F12)=(2,F5,F12); 269 (1,F5,F17)=(2,F5,F17); 270 (1,F5,F18)=(2,F5,F18); 271 (1,F10,F11)=(2,F10,F11); 272 (1,F10,F12)=(2,F10,F12); 273 (1,F10,F17)=(2,F10,F17); 274 (1,F10,F18)=(2,F10,F18); 275 (1,F11,F12)=(2,F11,F12); 276 (1,F11,F17)=(2,F11,F17); 277 (1,F11,F18)=(2,F11,F18); 278 (1,F12,F17)=(2,F12,F17); 279 (1,F12,F18)=(2,F12,F18); 280 (1,F17,F18)=(2,F17,F18); 281 (1,V16,F4)=(2,V16,F4); 282 (1,V18,F5)=(2,V18,F5); 283 (1,V19,F5)=(2,V19,F5); 284 (1,V20,F5)=(2,V20,F5); 285 (1,V21,F5)=(2,V21,F5); 286 (1,V22,F5)=(2,V22,F5); 287 (1,V41,F10)=(2,V41,F10); 288 (1,V42,F10)=(2,V42,F10); 289 (1,V43,F10)=(2,V43,F10); 290 (1,V44,F10)=(2,V44,F10); 291 (1,V45,F10)=(2,V45,F10); 292 (1,V46,F10)=(2,V46,F10); 293 (1,V48,F11)=(2,V48,F11); 294 (1,V49,F11)=(2,V49,F11); 295 (1,V50,F11)=(2,V50,F11); 296 (1,V52,F12)=(2,V52,F12); 297 (1,V53,F12)=(2,V53,F12); 298 (1,V54,F12)=(2,V54,F12); 299 (1,V79,F17)=(2,V79,F17); 300 (1,V80,F17)=(2,V80,F17); TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\ EQS/EM386 Licensee: Mikhail Koulikov 301 (1,V81,F17)=(2,V81,F17); 302 (1,V81,F18)=(2,V81,F18); 303 (1,E89,E89)=(2,E89,E89); 304 (1,E16,E16)=(2,E16,E16); 305 (1,E17,E17)=(2,E17,E17); 306 (1,E18,E18)=(2,E18,E18); 307 (1,E19,E19)=(2,E19,E19); 308 (1,E20,E20)=(2,E20,E20); 309 (1,E21,E21)=(2,E21,E21); 310 (1,E22,E22)=(2,E22,E22); 311 (1,E40,E40)=(2,E40,E40); 312 (1,E41,E41)=(2,E41,E41); 313 (1,E42,E42)=(2,E42,E42); 314 (1,E43,E43)=(2,E43,E43); 315 (1,E44,E44)=(2,E44,E44); 316 (1,E45,E45)=(2,E45,E45); 317 (1,E46,E46)=(2,E46,E46); 318 (1,E47,E47)=(2,E47,E47); 319 (1,E48,E48)=(2,E48,E48); 320 (1,E49,E49)=(2,E49,E49); 321 (1,E50,E50)=(2,E50,E50); 322 (1,E51,E51)=(2,E51,E51); 323 (1,E52,E52)=(2,E52,E52); 324 (1,E53,E53)=(2,E53,E53); 325 (1,E54,E54)=(2,E54,E54); 326 (1,E78,E78)=(2,E78,E78); 327 (1,E79,E79)=(2,E79,E79); 328 (1,E80,E80)=(2,E80,E80); 329 (1,E81,E81)=(2,E81,E81); 330 (1,E82,E82)=(2,E82,E82); 331 (1,E83,E83)=(2,E83,E83); 332 (1,E84,E84)=(2,E84,E84); 333 (1,E85,E85)=(2,E85,E85); 334 (1,E86,E86)=(2,E86,E86); 335 (1,E87,E87)=(2,E87,E87); 336 (1,E88,E88)=(2,E88,E88); 337 (1,V83,F18)=(2,V83,F18); 338 (1,V84,F18)=(2,V84,F18); 339 (1,V85,F18)=(2,V85,F18); 340 (1,V86,F18)=(2,V86,F18); 341 (1,V87,F18)=(2,V87,F18); 342 (1,V88,F18)=(2,V88,F18); 343 /LMTEST 344 /END 344 CUMULATED RECORDS OF INPUT MODEL FILE WERE DATA IS READ FROM C:\THESIS\EN_IMP~1\T_EIMP2.ESS THERE ARE 89 VARIABLES AND 459 CASES IT IS A RAW D

165


4

1

1F Q31G Q32A

855 2.0641 0.7673

34 1 0.4300 0.1731 1.0065

6 1.0844 .8864 1.0511

3 0.7610 .5959 0.9650

S

IVARIATE KURTOSIS = 0.1433

0.2889

ORMALIZED MULTIVARIATE KURTOSIS:

1

04 1740.4422

EQS/EM386 Licensee: Mikhail Koulikov SAMPLE STATISTICS BASED ON COMPLETE CASES UNIVARIATE STATISTICS --------------------- VARIABLE Q12C Q13 Q14A Q14B Q14C MEAN 2.0463 2.9955 2.7257 2.6569 2.5213 SKEWNESS (G1) 1.2849 0.2462 0.6930 0.7470 1.0043 KURTOSIS (G2) 2.7548 -0.5398 -0.3705 -0.0670 1.1043 STANDARD DEV. 0.8161 1.0676 1.0918 1.0172 0.860 VARIABLE Q14D Q14E Q31A Q31B Q31C MEAN 2.6796 2.6714 2.4633 2.4120 2.5485 SKEWNESS (G1) 0.7471 0.6618 0.6807 0.6057 0.5265 KURTOSIS (G2) 0.4712 0.2002 -0.2444 -0.3215 -0.3329 STANDARD DEV. 0.8929 0.9523 1.1762 1.1261 1.123 VARIABLE Q31D Q31E Q3 MEAN 2.4653 2.2348 2.6058 2.4699 2.2744 SKEWNESS (G1) 0.6306 0.7215 0.3958 0.5201 0.8 KURTOSIS (G2) -0.3384 0.0126 -0.6201 -0.5117 STANDARD DEV. 1.1571 1.0548 1.1777 1.1690 VARIABLE Q32B Q32C Q32D Q33 Q MEAN 2.4521 2.4240 2.3980 2.9835 2.767 SKEWNESS (G1) 0.8656 0.9184 0.8323 0.3758 KURTOSIS (G2) 1.0557 1.2368 1.1149 -0.5042 - STANDARD DEV. 0.8343 0.8484 0.8579 1.0705 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.9610 2.6752 1.8093 2.0446 2.133 SKEWNESS (G1) 0.3007 0.5288 1.2328 1.0135 KURTOSIS (G2) -0.6359 0.0936 2.8132 1.0842 0 STANDARD DEV. 1.1372 1.0307 0.7615 0.9405 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3489 2.4360 2.5698 2.5774 2.287 SKEWNESS (G1) 0.7493 0.5879 0.7131 0.5796 KURTOSIS (G2) 0.7948 0.1410 0.2351 -0.1173 0 STANDARD DEV. 0.9588 0.9773 0.9994 1.0955 VARIABLE Q57 Q58 Q59 Q12AB_CO MEAN 2.3955 2.6554 2.6039 1.9840 SKEWNESS (G1) 0.6048 0.4939 0.3295 1.0128 KURTOSIS (G2) 0.3116 -0.3478 0.3661 2.4074 STANDARD DEV. 0.9649 1.1314 0.8864 0.7320 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 353.5560 NORMALIZED ESTIMATE = 76.5471 ELLIPTICAL THEORY KURTOSIS ESTIMATE ------------------------------------ MARDIA-BASED KAPPA = 0.2889 MEAN SCALED UN MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA= CASE NUMBERS WITH LARGEST CONTRIBUTION TO N --------------------------------------------------------------------------- CASE NUMBER 35 272 281 355 45 ESTIMATE 2345.8423 1590.5983 1690.5610 1755.89

166


(SELECTED FROM 89 VARIABLES)

1

EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 34 VARIABLES BASED ON 459 CASES. Q12C Q13 Q14A Q14B Q14C V 16 V 17 V 18 V 19 V 20 Q12C V 16 0.666 Q13 V 17 0.252 1.140 Q14A V 18 0.251 0.318 1.192 Q14B V 19 0.258 0.341 0.644 1.035 Q14C V 20 0.235 0.330 0.336 0.441 0.740 Q14D V 21 0.237 0.446 0.398 0.411 0.473 Q14E V 22 0.260 0.434 0.355 0.387 0.395 Q31A V 40 0.153 0.338 0.169 0.151 0.187 Q31B V 41 0.146 0.262 0.163 0.164 0.224 Q31C V 42 0.142 0.325 0.216 0.152 0.248 Q31D V 43 0.156 0.416 0.222 0.168 0.275 Q31E V 44 0.163 0.330 0.118 0.063 0.165 Q31F V 45 0.153 0.407 0.254 0.187 0.253 Q31G V 46 0.212 0.353 0.243 0.174 0.264 Q32A V 47 0.106 0.085 0.104 0.114 0.136 Q32B V 48 0.186 0.317 0.253 0.256 0.234 Q32C V 49 0.167 0.296 0.243 0.193 0.283 Q32D V 50 0.206 0.306 0.337 0.308 0.254 Q33 V 51 0.214 0.472 0.422 0.314 0.306 Q34 V 52 0.255 0.342 0.398 0.308 0.285 Q35 V 53 0.250 0.446 0.537 0.391 0.294 Q36 V 54 0.296 0.455 0.453 0.395 0.352 Q49 V 78 0.120 0.175 0.125 0.101 0.146 Q50 V 79 0.181 0.214 0.281 0.218 0.235 Q51 V 80 0.155 0.292 0.276 0.284 0.204 Q52 V 81 0.218 0.316 0.354 0.288 0.284 Q53 V 82 0.266 0.418 0.447 0.338 0.323 Q54 V 83 0.281 0.359 0.388 0.368 0.355 Q55 V 84 0.281 0.370 0.422 0.353 0.357 Q56 V 85 0.289 0.348 0.366 0.356 0.285 Q57 V 86 0.279 0.385 0.431 0.350 0.362 Q58 V 87 0.330 0.440 0.524 0.370 0.412 Q59 V 88 0.248 0.363 0.376 0.326 0.269 Q12AB_CO V 89 0.501 0.171 0.226 0.214 0.204 Q14D Q14E Q31A Q31B Q31C V 21 V 22 V 40 V 41 V 42 Q14D V 21 0.797 Q14E V 22 0.489 0.907 Q31A V 40 0.206 0.235 1.384 Q31B V 41 0.236 0.230 0.990 1.268 Q31C V 42 0.302 0.297 0.898 0.939 1.261 Q31D V 43 0.338 0.375 0.878 0.906 0.921 Q31E V 44 0.202 0.235 0.777 0.807 0.726 Q31F V 45 0.335 0.374 0.775 0.885 0.870 Q31G V 46 0.294 0.309 0.954 0.951 0.940 Q32A V 47 0.171 0.195 0.131 0.132 0.092 Q32B V 48 0.298 0.284 0.209 0.240 0.180 Q32C V 49 0.339 0.322 0.219 0.226 0.232 Q32D V 50 0.317 0.340 0.251 0.347 0.270 Q33 V 51 0.450 0.438 0.408 0.420 0.439 Q34 V 52 0.398 0.417 0.253 0.301 0.305 Q35 V 53 0.397 0.414 0.396 0.402 0.407 Q36 V 54 0.435 0.439 0.384 0.381 0.383 Q49 V 78 0.142 0.173 0.265 0.257 0.238 Q50 V 79 0.241 0.281 0.371 0.335 0.293 Q51 V 80 0.281 0.273 0.439 0.483 0.413 Q52 V 81 0.329 0.330 0.488 0.457 0.409 Q53 V 82 0.398 0.386 0.416 0.418 0.410 Q54 V 83 0.389 0.398 0.362 0.344 0.334 Q55 V 84 0.399 0.406 0.412 0.398 0.437 Q56 V 85 0.373 0.371 0.381 0.368 0.380 Q57 V 86 0.384 0.429 0.424 0.412 0.411 Q58 V 87 0.441 0.471 0.472 0.461 0.511 Q59 V 88 0.301 0.325 0.321 0.288 0.335 Q12AB_CO V 89 0.209 0.224 0.134 0.124 0.106 Q31D Q31E Q31F Q31G Q32A V 43 V 44 V 45 V 46 V 47 Q31D V 43 1.339 Q31E V 44 0.888 1.113 Q31F V 45 1.008 0.846 1.387 Q31G V 46 1.026 0.905 1.033 1.366 Q32A V 47 0.124 0.108 0.134 0.163 0.589 Q32B V 48 0.260 0.188 0.303 0.285 0.273 Q32C V 49 0.281 0.216 0.305 0.307 0.260 Q32D V 50 0.338 0.245 0.374 0.364 0.243 Q33 V 51 0.446 0.333 0.494 0.464 0.179 Q34 V 52 0.355 0.290 0.400 0.357 0.210 Q35 V 53 0.450 0.322 0.473 0.495 0.200 Q36 V 54 0.425 0.350 0.452 0.481 0.231 Q49 V 78 0.333 0.288 0.323 0.294 0.121 Q50 V 79 0.388 0.338 0.365 0.422 0.21 Q51 V 80 0.445 0.410 0.431 0.523 0.199 Q52 V 81 0.461 0.353 0.433 0.504 0.146 Q53 V 82 0.461 0.348 0.401 0.470 0.120

167

Q54 V 83 0.375 0.296 0.385 0.379 0.144 Q55 V 84 0.475 0.391 0.471 0.521 0.183

7

.411 0.136 187 0.143 0.183 0.117

V 80

2 76 0.155

0.999

0.931 743 0.723

0

B_CO

_CO V 89 0.242 0.279 0.218 0.536

ENTLER-WEEKS STRUCTURAL REPRESENTATION:

OF DEPENDENT VARIABLES = 34

DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 52 DEPENDENT V'S : 53 54 78 79 80 81 82 83 84 85 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 41 INDEPENDENT F'S : 4 5 10 11 12 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 40 41 42 INDEPENDENT E'S : 43 44 45 46 47 48 49 50 51 52 INDEPENDENT E'S : 53 54 78 79 80 81 82 83 84 85 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 90 NUMBER OF FIXED NONZERO PARAMETERS = 41 DATA IS READ FROM C:\THESIS\SP_IMP~1\T_SIMP2.ESS THERE ARE 89 VARIABLES AND 524 CASES IT IS A RAW DATA ESS FILE

Q56 V 85 0.396 0.326 0.413 0.444 0.184 Q57 V 86 0.489 0.381 0.451 0.496 0.14 Q58 V 87 0.550 0.393 0.515 0.547 0.207 Q59 V 88 0.377 0.318 0.381 0 Q12AB_CO V 89 0.157 0. Q32B Q32C Q32D Q33 Q34 V 48 V 49 V 50 V 51 V 52 Q32B V 48 0.696 Q32C V 49 0.449 0.720 Q32D V 50 0.471 0.459 0.736 Q33 V 51 0.401 0.463 0.504 1.146 Q34 V 52 0.385 0.434 0.462 0.708 1.013 Q35 V 53 0.437 0.485 0.464 0.818 0.698 Q36 V 54 0.426 0.477 0.496 0.669 0.599 Q49 V 78 0.135 0.130 0.166 0.175 0.208 Q50 V 79 0.241 0.202 0.235 0.328 0.297 Q51 V 80 0.261 0.227 0.289 0.385 0.343 Q52 V 81 0.318 0.326 0.364 0.477 0.361 Q53 V 82 0.353 0.362 0.371 0.534 0.436 Q54 V 83 0.319 0.322 0.342 0.488 0.401 Q55 V 84 0.336 0.392 0.417 0.540 0.492 Q56 V 85 0.364 0.356 0.392 0.493 0.458 Q57 V 86 0.368 0.412 0.400 0.528 0.487 Q58 V 87 0.421 0.451 0.482 0.657 0.563 Q59 V 88 0.334 0.370 0.355 0.467 0.363 Q12AB_CO V 89 0.188 0.155 0.196 0.200 0.217 Q35 Q36 Q49 Q50 Q51 V 53 V 54 V 78 V 79 Q35 V 53 1.293 Q36 V 54 0.826 1.062 Q49 V 78 0.127 0.141 0.580 Q50 V 79 0.319 0.328 0.392 0.885 Q51 V 80 0.362 0.347 0.388 0.681 1.105 Q52 V 81 0.505 0.531 0.310 0.565 0.602 Q53 V 82 0.612 0.661 0.177 0.367 0.443 Q54 V 83 0.574 0.555 0.176 0.332 0.378 Q55 V 84 0.521 0.612 0.283 0.435 0.450 Q56 V 85 0.527 0.645 0.185 0.362 0.385 Q57 V 86 0.611 0.667 0.244 0.411 0.426 Q58 V 87 0.766 0.761 0.292 0.490 0.526 Q59 V 88 0.506 0.582 0.164 0.316 0.38 Q12AB_CO V 89 0.212 0.271 0.125 0.1 Q52 Q53 Q54 Q55 Q56 V 81 V 82 V 83 V 84 V 85 Q52 V 81 0.919 Q53 V 82 0.597 0.955 Q54 V 83 0.496 0.645 Q55 V 84 0.666 0.674 0.644 1.200 Q56 V 85 0.541 0.677 0.597 0.736 Q57 V 86 0.642 0.728 0.604 0. Q58 V 87 0.730 0.789 0.737 0.880 0.793 Q59 V 88 0.480 0.565 0.454 0.543 0.535 Q12AB_CO V 89 0.187 0.230 0.240 0.244 0.24 Q57 Q58 Q59 Q12A V 86 V 87 V 88 V 89 Q57 V 86 0.931 Q58 V 87 0.868 1.280 Q59 V 88 0.591 0.676 0.786 Q12AB B NUMBER DEPENDENT V'S : 16 17 18 19 20 21 22 40 41 42

168

TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 10

SED ON COMPLETE CASES

Q14A Q14B Q14C 2.5646 2.4495

376 0.9837 440 .9523

C

4455 754 .0845

A

2281 7548 .7959

.8609 9357 0.9152

.9446 8905 0.9173

IS (G2) 2.5850 1.4789 1.0254 1.0156 1.4527 0.9192

Q58 Q59 Q12AB_CO 2.4621 2.1368

98 1.2458 96

VARIATE KURTOSIS = 0.3548

PPA= 0.4507

ORMALIZED MULTIVARIATE KURTOSIS:

14

72 3803.2697

EQS/EM386 Licensee: Mikhail Koulikov SAMPLE STATISTICS BA UNIVARIATE STATISTICS --------------------- VARIABLE Q12C Q13 MEAN 2.1452 2.6965 2.2206 SKEWNESS (G1) 1.3891 0.5783 1.3119 0.8 KURTOSIS (G2) 2.7887 -0.1237 2.0481 0.1406 0.8 STANDARD DEV. 0.8455 1.0454 0.8959 1.0453 0 VARIABLE Q14D Q14E Q31A Q31B Q31 MEAN 2.5451 2.4848 2.7802 2.8403 2.7775 SKEWNESS (G1) 0.9346 1.0151 0.4534 0.3549 0. KURTOSIS (G2) 0.8134 0.9112 -0.5514 -0.5155 -0.3 STANDARD DEV. 0.9372 0.9587 1.1498 1.0947 1 VARIABLE Q31D Q31E Q31F Q31G Q32 MEAN 2.5928 2.5787 2.7023 2.6083 2.1897 SKEWNESS (G1) 0.6602 0.6850 0.4831 0.5893 1. KURTOSIS (G2) -0.0802 -0.0407 -0.4307 -0.3730 2. STANDARD DEV. 1.0582 1.0532 1.1304 1.1576 0 VARIABLE Q32B Q32C Q32D Q33 Q34 MEAN 2.2059 2.2134 2.2424 2.4903 2.4678 SKEWNESS (G1) 1.1591 1.0335 1.0235 0.9294 0 KURTOSIS (G2) 2.3899 2.7005 2.0292 0.7101 0. STANDARD DEV. 0.8262 0.7121 0.8069 0.9954 VARIABLE Q35 Q36 Q49 Q50 Q51 MEAN 2.5802 2.3638 1.8900 2.3733 2.3009 SKEWNESS (G1) 0.7756 0.9311 1.2237 0.9512 0 KURTOSIS (G2) 0.4536 1.1508 3.4898 0.9744 0. STANDARD DEV. 0.9894 0.9280 0.6985 0.9077 VARIABLE Q52 Q53 Q54 Q55 Q56 MEAN 2.3230 2.2813 2.3224 2.3833 2.2631 SKEWNESS (G1) 1.0258 1.0365 0.8822 0.8659 1.0318 KURTOS STANDARD DEV. 0.7830 0.8915 0.8647 0.9125 VARIABLE Q57 MEAN 2.2874 2.5839 SKEWNESS (G1) 0.9808 0.6071 0.66 KURTOSIS (G2) 1.3401 0.0862 1.0939 2.57 STANDARD DEV. 0.8986 1.0086 0.9026 0.7738 MULTIVARIATE KURTOSIS --------------------- MARDIA'S COEFFICIENT (G2,P) = 551.6557 NORMALIZED ESTIMATE = 127.6139 ELLIPTICAL THEORY KURTOSIS ESTIMATES ------------------------------------ MARDIA-BASED KAPPA = 0.4507 MEAN SCALED UNI MARDIA-BASED KAPPA IS USED IN COMPUTATION. KA CASE NUMBERS WITH LARGEST CONTRIBUTION TO N --------------------------------------------------------------------------- CASE NUMBER 147 206 235 510 5 ESTIMATE 2968.2113 2661.4384 3206.6996 3151.31

169


LES (SELECTED FROM 89 VARIABLES)

0.297 0.488 1.093 0.419 0.528 0.907

0.503 0.589 0.480

51 213

0.252 0.146 0.202 0.242 0.206 0.285 0.290

0.265 0.301 0.308

28

10 0.240 1.322

.176 0.308 0.777 0.767 0.877

0.742 0.733 0.730 0.827 0.835

0.856

0.374 361 0.345

0.133 0.148 0.089 0.143 0.292 0.310 0.240

0.272 0.240 41

0.297

Q31G Q32A

0.329 0.633 V 48 0.306 0.352 0.339 0.350 0.399

.281 0.324

0.339 0.189 0.178 0.136

Q51 V 80 0.276 0.318 0.314 0.383 0.217 Q52 V 81 0.296 0.252 0.279 0.308 0.176 Q53 V 82 0.297 0.335 0.386 0.382 0.254

EQS/EM386 Licensee: Mikhail Koulikov COVARIANCE MATRIX TO BE ANALYZED: 34 VARIAB BASED ON 524 CASES. Q12C Q13 Q14A Q14B Q14C V 16 V 17 V 18 V 19 V 20 Q12C V 16 0.715 Q13 V 17 0.295 1.093 Q14A V 18 0.293 0.283 0.803 Q14B V 19 0.251 Q14C V 20 0.318 0.370 Q14D V 21 0.266 0.324 0.423 Q14E V 22 0.273 0.261 0.399 0.435 Q31A V 40 0.271 0.462 0.291 0.336 0.318 Q31B V 41 0.281 0.394 0.185 0.297 0.275 Q31C V 42 0.299 0.393 0.225 0.293 0.332 Q31D V 43 0.294 0.394 0.225 0.258 0.317 Q31E V 44 0.307 0.404 0.279 0.298 0.350 Q31F V 45 0.315 0.418 0.213 0.283 0.367 Q31G V 46 0.302 0.523 0.267 0.328 0.367 Q32A V 47 0.229 0.213 0.227 0.267 0.244 Q32B V 48 0.218 0.218 0.285 0.294 0.288 Q32C V 49 0.168 0.229 0.269 0.167 0.194 Q32D V 50 0.220 0.200 0.239 0.241 0.248 Q33 V 51 0.295 0.400 0.292 0.373 0.343 Q34 V 52 0.262 0.410 0.277 0.273 0.326 Q35 V 53 0.267 0.386 0.306 0.422 0.285 Q36 V 54 0.242 0.367 0.333 0.384 0.303 Q49 V 78 0.164 0.142 0.146 0.135 0.158 Q50 V 79 0.185 0.320 0.218 0.261 0.2 Q51 V 80 0.147 0.265 0.239 0.228 0. Q52 V 81 0.192 Q53 V 82 0.255 0.272 Q54 V 83 0.227 0.332 0.248 Q55 V 84 0.198 0.297 0.283 0.288 Q56 V 85 0.199 0.310 0.245 0.258 0.282 Q57 V 86 0.212 0.313 0.248 0.305 0.270 Q58 V 87 0.232 0.384 0.267 0.284 0.249 Q59 V 88 0.265 0.289 0.230 0.217 0.221 Q12AB_CO V 89 0.510 0.321 0.251 0.233 0.3 Q14D Q14E Q31A Q31B Q31C V 21 V 22 V 40 V 41 V 42 Q14D V 21 0.878 Q14E V 22 0.510 0.919 Q31A V 40 0.3 Q31B V 41 0.280 0.258 0.882 1.198 Q31C V 42 0.337 0.319 0.810 0.828 1 Q31D V 43 0.350 Q31E V 44 0.344 0.281 Q31F V 45 0.356 0.307 0.772 Q31G V 46 0.330 0.288 0.856 0.832 Q32A V 47 0.232 0.270 0.230 0.226 0.258 Q32B V 48 0.275 0.285 0.240 0.249 0.291 Q32C V 49 0.210 0.224 0.250 0.239 0.231 Q32D V 50 0.238 0.300 0.274 0.294 0.310 Q33 V 51 0.299 0.348 0.420 0.409 0.448 Q34 V 52 0.294 0.299 0.365 0.403 0.399 Q35 V 53 0.265 0.298 0.438 0.395 Q36 V 54 0.265 0.300 0.413 0. Q49 V 78 0.120 Q50 V 79 0.241 0.183 Q51 V 80 0.135 0.183 0.324 Q52 V 81 0.201 0.200 0.327 0.260 0.2 Q53 V 82 0.237 0.260 0.349 0.328 0.310 Q54 V 83 0.255 0.238 0.386 0.363 0.329 Q55 V 84 0.236 0.243 0.345 0.315 0.356 Q56 V 85 0.244 0.225 0.334 0.303 0.315 Q57 V 86 0.258 0.266 0.328 0.300 Q58 V 87 0.267 0.242 0.496 0.489 0.432 Q59 V 88 0.228 0.206 0.317 0.332 0.305 Q12AB_CO V 89 0.288 0.287 0.245 0.239 0.267 Q31D Q31E Q31F V 43 V 44 V 45 V 46 V 47 Q31D V 43 1.120 Q31E V 44 0.807 1.109 Q31F V 45 0.873 0.828 1.278 Q31G V 46 0.828 0.813 0.904 1.340 Q32A V 47 0.269 0.285 0.279 Q32B Q32C V 49 0.267 0.274 0.302 0 Q32D V 50 0.295 0.303 0.335 0.380 0.347 Q33 V 51 0.453 0.450 0.507 0.506 0.354 Q34 V 52 0.427 0.421 0.479 0.470 0.273 Q35 V 53 0.346 0.399 0.451 0.501 0.308 Q36 V 54 0.365 0.392 0.414 0.447 Q49 V 78 0.177 0.211 Q50 V 79 0.303 0.286 0.342 0.334 0.208

170

Q54 V 83 0.332 0.381 0.429 0.411 0.235 Q55 V 84 0.335 0.298 0.403 0.431 0.234

7

.351 0.216 240 0.289 0.257 0.197

V 80

2 86 0.152

0.478 0.833

0.661 515 0.570

_CO V 89 0.203 0.264 0.243 0.222 0.248

2AB_CO

_CO V 89 0.231 0.228 0.248 0.599

'S : 16 17 18 19 20 21 22 40 41 42

DEPENDENT V'S : 53 54 78 79 80 81 82 83 84 85 DEPENDENT V'S : 86 87 88 89 NUMBER OF INDEPENDENT VARIABLES = 41 INDEPENDENT F'S : 4 5 10 11 12 17 18 INDEPENDENT E'S : 16 17 18 19 20 21 22 40 41 42 INDEPENDENT E'S : 43 44 45 46 47 48 49 50 51 52 INDEPENDENT E'S : 53 54 78 79 80 81 82 83 84 85 INDEPENDENT E'S : 86 87 88 89 NUMBER OF FREE PARAMETERS = 90 NUMBER OF FIXED NONZERO PARAMETERS = 41 3RD STAGE OF COMPUTATION REQUIRED 155411 WORDS OF MEMORY. PROGRAM ALLOCATED 900000 WORDS DETERMINANT OF INPUT MATRIX IN GROUP 1 IS 0.43722E-11 DETERMINANT OF INPUT MATRIX IN GROUP 2 IS 0.15676E-11

Q56 V 85 0.293 0.296 0.397 0.339 0.247 Q57 V 86 0.290 0.318 0.388 0.366 0.28 Q58 V 87 0.421 0.434 0.483 0.531 0.195 Q59 V 88 0.316 0.376 0.397 0 Q12AB_CO V 89 0.264 0. Q32B Q32C Q32D Q33 Q34 V 48 V 49 V 50 V 51 V 52 Q32B V 48 0.683 Q32C V 49 0.342 0.507 Q32D V 50 0.395 0.312 0.651 Q33 V 51 0.425 0.336 0.464 0.991 Q34 V 52 0.360 0.310 0.320 0.607 0.838 Q35 V 53 0.377 0.287 0.393 0.593 0.491 Q36 V 54 0.407 0.320 0.382 0.483 0.429 Q49 V 78 0.133 0.107 0.155 0.166 0.117 Q50 V 79 0.251 0.211 0.216 0.313 0.236 Q51 V 80 0.210 0.187 0.216 0.314 0.238 Q52 V 81 0.156 0.152 0.182 0.253 0.201 Q53 V 82 0.260 0.200 0.252 0.317 0.316 Q54 V 83 0.251 0.243 0.302 0.360 0.319 Q55 V 84 0.285 0.250 0.266 0.370 0.311 Q56 V 85 0.249 0.245 0.251 0.330 0.309 Q57 V 86 0.279 0.235 0.263 0.348 0.312 Q58 V 87 0.233 0.230 0.302 0.425 0.358 Q59 V 88 0.218 0.214 0.196 0.289 0.287 Q12AB_CO V 89 0.215 0.166 0.200 0.298 0.284 Q35 Q36 Q49 Q50 Q51 V 53 V 54 V 78 V 79 Q35 V 53 0.979 Q36 V 54 0.617 0.861 Q49 V 78 0.180 0.162 0.488 Q50 V 79 0.329 0.338 0.257 0.824 Q51 V 80 0.333 0.319 0.291 0.497 0.841 Q52 V 81 0.230 0.253 0.199 0.323 0.300 Q53 V 82 0.366 0.344 0.194 0.326 0.322 Q54 V 83 0.380 0.369 0.210 0.410 0.385 Q55 V 84 0.365 0.366 0.188 0.417 0.360 Q56 V 85 0.361 0.355 0.217 0.398 0.314 Q57 V 86 0.407 0.376 0.228 0.359 0.334 Q58 V 87 0.515 0.419 0.189 0.459 0.407 Q59 V 88 0.351 0.342 0.181 0.365 0.29 Q12AB_CO V 89 0.228 0.224 0.145 0.1 Q52 Q53 Q54 Q55 Q56 V 81 V 82 V 83 V 84 V 85 Q52 V 81 0.613 Q53 V 82 0.368 0.795 Q54 V 83 0.340 0.487 0.748 Q55 V 84 0.325 0.481 Q56 V 85 0.345 0.523 0.511 0.566 0.845 Q57 V 86 0.373 0.545 0.495 0.508 Q58 V 87 0.408 0.499 0.531 0. Q59 V 88 0.316 0.460 0.407 0.427 0.490 Q12AB Q57 Q58 Q59 Q1 V 86 V 87 V 88 V 89 Q57 V 86 0.807 Q58 V 87 0.568 1.017 Q59 V 88 0.510 0.516 0.815 Q12AB BENTLER-WEEKS STRUCTURAL REPRESENTATION: NUMBER OF DEPENDENT VARIABLES = 34 DEPENDENT V DEPENDENT V'S : 43 44 45 46 47 48 49 50 51 52

171

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 12 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

WERE ENCOUNTERED DURING OPTIMIZATION.

OMITTED]

05/11/03 PAGE : 13

OUP 1

TION THEORY)

9 0.113 0.151 0.006 -0.096

011 0.022 0.062 -0.020 -0.006

0.047 0.037 -0.048 0.025 0.021

0.010

085

7 0.039 -0.025 -0.026 -0.022

046 0.056 0.027 -0.011 -0.050

0.071 0.107 0.048 0.107 0.064

0.037

126

PARAMETER ESTIMATES APPEAR IN ORDER, NO SPECIAL PROBLEMS RESIDUAL COVARIANCE MATRIX (S-SIGMA) [ TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GR MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBU STANDARDIZED RESIDUAL MATRIX: Q12C Q13 Q14A Q14B Q14C V 16 V 17 V 18 V 19 V 20 Q12C V 16 -0.039 Q13 V 17 0.040 0.022 Q14A V 18 0.006 -0.017 0.174 Q14B V 19 -0.014 -0.029 0.201 -0.030 Q14C V 20 -0.053 -0.049 -0.095 -0.029 -0.120 Q14D V 21 -0.065 0.057 -0.046 -0.083 -0.023 Q14E V 22 -0.001 0.073 -0.050 -0.062 -0.069 Q31A V 40 -0.068 0.088 -0.069 -0.110 -0.097 Q31B V 41 -0.082 0.026 -0.079 -0.107 -0.068 Q31C V 42 -0.086 0.079 -0.036 -0.117 -0.042 Q31D V 43 -0.078 0.143 -0.039 -0.110 -0.024 Q31E V 44 -0.051 0.102 -0.108 -0.190 -0.115 Q31F V 45 -0.080 0.133 -0.013 -0.093 -0.047 Q31G V 46 -0.028 0.084 -0.030 -0.114 -0.047 Q32A V 47 -0.070 -0.096 -0.096 -0.114 -0.103 Q32B V 48 -0.032 0.103 -0.002 -0.028 -0.068 Q32C V 49 -0.029 0.103 0.014 -0.069 0.038 Q32D V 50 0.000 0.090 0.090 0.035 -0.035 Q33 V 51 -0.057 0.155 0.076 -0.048 -0.069 Q34 V 52 0.028 0.077 0.095 -0.013 -0.047 Q35 V 53 -0.02 Q36 V 54 0.049 0.155 0.117 0.040 -0.005 Q49 V 78 0.025 0.079 0.000 -0.047 0. Q50 V 79 -0.002 Q51 V 80 -0.038 0.085 Q52 V 81 0.015 0.101 0.109 Q53 V 82 0.025 0.160 0.152 0.026 Q54 V 83 0.066 0.119 0.116 0.079 0.075 Q55 V 84 0.033 0.097 0.110 0.031 0.038 Q56 V 85 0.040 0.083 0.065 0.030 -0.054 Q57 V 86 0.015 0.109 0.117 0.012 0.025 Q58 V 87 0.050 0.125 0.159 0.009 0.050 Q59 V 88 0.048 0.153 0.135 0.062 -0.004 Q12AB_CO V 89 -0.008 -0.044 -0.008 -0.056 -0. Q14D Q14E Q31A Q31B Q31C V 21 V 22 V 40 V 41 V 42 Q14D V 21 -0.054 Q14E V 22 0.021 -0.007 Q31A V 40 -0.087 -0.035 0.024 Q31B V 41 -0.066 -0.044 0.122 0.029 Q31C V 42 0.001 0.018 0.055 0.079 0.036 Q31D V 43 0.025 0.079 0.015 0.026 0.040 Q31E V 44 -0.084 -0.024 0.005 0.020 -0.046 Q31F V 45 0.020 0.077 -0.061 0.009 -0.001 Q31G V 46 -0.030 0.009 0.044 0.034 0.027 Q32A V 47 -0.062 -0.002 -0.084 -0.089 -0.135 Q32B V 48 0.005 0.016 -0.077 -0.053 -0.116 Q32C V 49 0.096 0.096 -0.038 -0.036 -0.030 Q32D V 50 0.032 0.087 -0.031 0.063 -0.017 Q33 V 51 0.068 0.082 -0.001 0.004 0.020 Q34 V 52 0.066 0.110 -0.091 -0.057 -0.053 Q35 V 53 -0.00 Q36 V 54 0.068 0.098 -0.009 -0.018 -0.016 Q49 V 78 -0.004 0.055 0.077 0.067 0. Q50 V 79 -0.012 Q51 V 80 0.025 0.036 Q52 V 81 0.059 0.081 0.135 Q53 V 82 0.079 0.090 0.044 0.043 Q54 V 83 0.096 0.126 0.022 0.002 -0.006 Q55 V 84 0.065 0.094 0.031 0.016 0.049 Q56 V 85 0.033 0.059 -0.001 -0.018 -0.007 Q57 V 86 0.032 0.110 0.026 0.010 0.011 Q58 V 87 0.061 0.114 0.039 0.027 0.067 Q59 V 88 0.021 0.075 0.005 -0.033 0.015 Q12AB_CO V 89 -0.092 -0.034 -0.084 -0.104 -0.

172

Q31D Q31E Q31F Q31G Q32A V 43 V 44 V 45 V 46 V 47 Q31D V 43 0.087 Q31E V 44 0.062 0.002 Q31F V 45 0.075 0.027 0.042 Q31G V 46 0.063 0.049 0.066 0.010 Q32A V 47 -0.105 -0.107 -0.093 -0.070 -0.041 Q32B V 48 -0.041 -0.093 0.003 -0.028 -0.066

8 0.106

-0.048 0.050 0.013 -0.157 0.017 -0.032 -0.079

0.019 -0.141 -0.084

044

V 48 0.010

0.086 0.089 0.072 0.105 0.121 0.092

0.107 0.081 0.040

-0.004

6 Q49 Q50 Q51 8 V 79 V 80

.008 0.027 0.055

.026 -0.011 0.195 0.019 0.013 0.016

0.055 0.062 0.075 -0.004 0.058

-0.030

Q56

0.100 0.076

.016 -0.006

ABSOLUTE STANDARDIZED RESIDUALS = 0.0614 0.0607

Q32C V 49 0.011 -0.030 0.034 0.026 -0.03 Q32D V 50 0.041 -0.025 0.075 0.055 - Q33 V 51 0.012 Q34 V 52 -0.021 -0.047 Q35 V 53 -0.001 -0.069 0.016 Q36 V 54 0.007 -0.022 0.029 0.040 Q49 V 78 0.143 0.127 0.129 0.088 0.036 Q50 V 79 0.026 0.016 0.005 0.044 0.052 Q51 V 80 0.062 0.070 0.049 0.113 0.026 Q52 V 81 0.097 0.035 0.070 0.122 -0.060 Q53 V 82 0.068 0.003 0.014 0.062 -0.138 Q54 V 83 0.018 -0.021 0.025 0.009 -0.081 Q55 V 84 0.066 0.034 0.061 0.088 -0.053 Q56 V 85 -0.006 -0.034 0.009 0.023 -0.067 Q57 V 86 0.066 0.008 0.030 0.056 -0.127 Q58 V 87 0.083 -0.003 0.054 0.066 -0.057 Q59 V 88 0.044 0.021 0.046 0.064 -0.084 Q12AB_CO V 89 -0.071 -0.011 -0.086 -0.050 -0. Q32B Q32C Q32D Q33 Q34 V 48 V 49 V 50 V 51 V 52 Q32B Q32C V 49 0.079 0.158 Q32D V 50 0.055 0.095 0.062 Q33 V 51 -0.027 Q34 V 52 0.013 0.113 Q35 V 53 -0.009 0.085 0.022 Q36 V 54 0.018 0.121 0.100 0.032 Q49 V 78 -0.004 0.009 0.045 -0.008 0.063 Q50 V 79 0.003 -0.017 -0.002 0.012 0.020 Q51 V 80 0.018 0.006 0.051 0.053 0.054 Q52 V 81 0.070 0.110 0.126 0.104 0.037 Q53 V 82 0.056 0.102 0.078 0.078 0.038 Q54 V 83 0.042 0.078 0.070 0.067 0.034 Q55 V 84 0.024 0.116 0.112 0.066 0.078 Q56 V 85 0.051 0.079 0.088 0.019 0.043 Q57 V 86 0.043 0.136 0.084 0.037 0.058 Q58 V 87 0.070 0.137 0.134 0.113 0.092 Q59 V 88 0.092 0.173 0.120 0.080 0.021 Q12AB_CO V 89 -0.014 -0.035 0.001 -0.063 Q35 Q3 V 53 V 54 V 7 Q35 V 53 0.130 Q36 V 54 0.138 0.101 Q49 V 78 -0.073 -0.044 0.085 Q50 V 79 -0.011 0.024 0.094 0.037 Q51 V 80 0.018 0.031 0.066 0.102 0.127 Q52 V 81 0.107 0.176 0.083 0.146 0.155 Q53 V 82 0.125 0.222 -0.048 -0.002 0.062 Q54 V 83 0.120 0.148 -0.026 -0 Q55 V 84 0.030 0.147 0.079 0.057 Q56 V 85 0.028 0.189 -0.051 -0 Q57 V 86 0.089 Q58 V 87 0.171 0.221 Q59 V 88 0.096 0.223 -0.030 Q12AB_CO V 89 -0.060 0.040 0.045 0.005 Q52 Q53 Q54 Q55 V 81 V 82 V 83 V 84 V 85 Q52 V 81 0.178 Q53 V 82 0.150 0.089 Q54 V 83 0.078 0.116 0.134 Q55 V 84 0.190 0.080 0.093 0.163 Q56 V 85 0.068 0.079 0.043 0.114 0.049 Q57 V 86 0.159 0.111 0.030 Q58 V 87 0.187 0.113 0.111 0.163 0.090 Q59 V 88 0.099 0.077 -0.006 0.035 0 Q12AB_CO V 89 -0.012 -0.004 0.034 0.008 Q57 Q58 Q59 Q12AB_CO V 86 V 87 V 88 V 89 Q57 V 86 0.071 Q58 V 87 0.137 0.109 Q59 V 88 0.061 0.101 -0.020 Q12AB_CO V 89 -0.016 0.013 0.025 -0.063 AVERAGE AVERAGE OFF-DIAGONAL ABSOLUTE STANDARDIZED RESIDUALS =

173

LARGEST STANDARDIZED RESIDUALS: V 88,V 54 V 82,V 54 V 87,V 54 V 19,V 18 V 86,V 54

0.163

05/11/03 PAGE : 14

ANALYSIS, INFORMATION IN GROUP 1

RMAL DISTRIBUTION THEORY)

ESIDUALS

0.223 0.222 0.221 0.201 0.195 V 84,V 81 V 44,V 19 V 85,V 54 V 87,V 81 V 81,V 81 0.190 -0.190 0.189 0.187 0.178 V 81,V 54 V 18,V 18 V 88,V 49 V 87,V 53 V 84,V 84 0.176 0.174 0.173 0.171 V 87,V 84 V 82,V 17 V 86,V 81 V 87,V 18 V 49,V 49 0.163 0.160 0.159 0.159 0.158 TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION MAXIMUM LIKELIHOOD SOLUTION (NO DISTRIBUTION OF STANDARDIZED RESIDUALS ---------------------------------------- ! ! 300- * - ! * ! ! * ! ! * ! ! * ! RANGE FREQ PERCENT 225- * - ! * ! 1 -0.5 - -- 0 0.00% ! * ! 2 -0.4 - -0.5 0 0.00% ! * * ! 3 -0.3 - -0.4 0 0.00% ! * * ! 4 -0.2 - -0.3 0 0.00% 150- * * - 5 -0.1 - -0.2 22 3.70% ! * * ! 6 0.0 - -0.1 180 30.25% ! * * ! 7 0.1 - 0.0 301 50.59% ! * * ! 8 0.2 - 0.1 88 14.79% ! * * * ! 9 0.3 - 0.2 4 0.67% 75- * * * - A 0.4 - 0.3 0 0.00% ! * * * ! B 0.5 - 0.4 0 0.00% ! * * * ! C ++ - 0.5 0 0.00% ! * * * ! ------------------------------- ! * * * * ! TOTAL 595 100.00% ---------------------------------------- 1 2 3 4 5 6 7 8 9 A B C EACH "*" REPRESENTS 15 R

174



RMAL DISTRIBUTION THEORY)

T STATISTICS

F5 + 1.000 E22

+ 1.000 E40

Q31B =V41 = 1.016*F10 + 1.000 E41

S AND TEST STATISTICS (CONTINUED)


IN GROUP 1

D SOLUTION (NORMAL DISTRIBUTION THEORY) 0 E45

*F11 + 1.000 E49

Q34 =V52 = .880*F12 + 1.000 E52

Q35 =V53 = 1.048*F12 + 1.000 E53

26.485

E54

F17 + 1.000 E78

F17 + 1.000 E79 .098 17.802 MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS (CONTINUED)

EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION MAXIMUM LIKELIHOOD SOLUTION (NO MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TES

Q12C =V16 = 1.056*F4 + 1.000 E16 .043 24.535

Q13 =V17 = 1.000 F5 + 1.000 E17

Q14A =V18 = 1.131*F5 + 1.000 E18 .081 13.939

Q14B =V19 = 1.242*F5 + 1.000 E19 .086 14.375

Q14C =V20 = 1.254*F5 + 1.000 E20 .081 15.407

Q14D =V21 = 1.308*F5 + 1.000 E21 .084 15.659

Q14E =V22 = 1.203* .082 14.729

Q31A =V40 = 1.000 F10

.036 28.471

Q31C =V42 = 1.013*F10 + 1.000 E42 .035 28.605

Q31D =V43 = 1.053*F10 + 1.000 E43 .035 29.952

Q31E =V44 = .946*F10 + 1.000 E44 .034 27.750 MEASUREMENT EQUATIONS WITH STANDARD ERROR TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 MULTIPLE POPULATION ANALYSIS, INFORMATION MAXIMUM LIKELIHOO

Q31F =V45 = 1.054*F10 + 1.00 .037 28.438

Q31G =V46 = 1.095*F10 + 1.000 E46 .037 29.515

Q32A =V47 = 1.000 F11 + 1.000 E47

Q32B =V48 = 1.380*F11 + 1.000 E48 .073 18.956

Q32C =V49 = 1.246 .067 18.498

Q32D =V50 = 1.369*F11 + 1.000 E50 .073 18.847

Q33 =V51 = 1.000 F12 + 1.000 E51

.036 24.250

.040

Q36 =V54 = .966*F12 + 1.000 .036 26.499

Q49 =V78 = 1.000

Q50 =V79 = 1.736*

175

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 17 Licensee: Mikhail Koulikov

LTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1

AL DISTRIBUTION THEORY) F17 + 1.000 E80

F18 + 1.000 E84

F18 + 1.000 E86

9

05/11/03 PAGE : 18

IN GROUP 1

RIBUTION THEORY)

I .037 I I 8.171 I I I I F10 - F10 .816*I I .057 I I 14.268 I I I I F11 - F11 .228*I I .023 I I 9.965 I I I I F12 - F12 .656*I I .046 I I 14.184 I I I I F17 - F17 .187*I I .020 I I 9.429 I I I I F18 - F18 .576*I I .038 I I 15.299 I I I

EQS/EM386 MU MAXIMUM LIKELIHOOD SOLUTION (NORM

Q51 =V80 = 1.785* .102 17.572

Q52 =V81 = .745*F17 + .519*F18 + 1.000 E81 .079 .040 9.416 12.964

Q53 =V82 = 1.000 F18 + 1.000 E82

Q54 =V83 = .922*F18 + 1.000 E83 .034 26.736

Q55 =V84 = 1.021* .037 27.741

Q56 =V85 = 1.047*F18 + 1.000 E85 .033 31.496

Q57 =V86 = 1.082* .032 33.713

Q58 =V87 = 1.151*F18 + 1.000 E87 .038 30.095

Q59 =V88 = .865*F18 + 1.000 E88 .033

25.810

Q12AB_CO=V89 = 1.000 F4 + 1.000 E8 TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION MAXIMUM LIKELIHOOD SOLUTION (NORMAL DIST VARIANCES OF INDEPENDENT VARIABLES ---------------------------------- V F --- --- I F4 - F4 .479*I I .030 I I 15.747 I I I I F5 - F5 .299*I

176

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 19 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) VARIANCES OF INDEPENDENT VARIABLES

I I

I34.EDS 05/11/03 PAGE : 20

LTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1

ON THEORY)

---------------------------------- E D --- --- E16 - Q12C .158*I I .020 I I 7.915 I I I I

E17 - Q13 .815*I I .039 I I 20.918 I I I I

E18 - Q14A .602*I I .030 I I

20.009 I I I I

E19 - Q14B .604*I I .031 I I

19.570 I I I I

E20 - Q14C .359*I I .020 I I 17.689 I I I I

E21 - Q14D .328*I I .019 I I 16.837 I I I I

E22 - Q14E .480*I I .025 I I 19.102 I I I I

E40 - Q31A .535*I I .027 I I 20.016 I I I I

E41 - Q31B .390*I I .020 I I

19.119 I I I I

E42 - Q31C .379*I I .020 I I

19.047 I I I I

E43 - Q31D .318*I I .018 I I

18.155 I I I I

E44 - Q31E .382*I I .020 I I 19.471 TITLE: Model created by EQS 5.7b -- 5F389 EQS/EM386 Licensee: Mikhail Koulikov MU MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTI

CONTINUED) VARIANCES OF INDEPENDENT VARIABLES ( ---------------------------------------------- E45 - Q31F .422*I I .022 I I 19.136 I I I I

E46 - Q31G .375*I I .020 I I 18.481 I I I I

E47 - Q32A .384*I I .019 I I 20.199 I I I I

E48 - Q32B .254*I I .015 I I 16.400 I I I I

E49 - Q32C .252*I I .014 I I 17.455 I I I I

E50 - Q32D .263*I I .016 I I 16.686 I I I I

E51 - Q33 .407*I I .023 I I 17.991 I I I I

E52 - Q34 .412*I I .022 I I 18.988 I I I I

E53 - Q35 .405*I I .023 I I 17.546 I I I I

E54 - Q36 .343*I I .020 I I 17.535 I I I I

E78 - Q49 .344*I I .017 I I 19.793 I I I I

8*I I E79 - Q50 .28 .022 I I 13.269 I I

177

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 21 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

I

I I


----------------------------------

I I 10.627 I

I .207*I

I F5 - F5 .026 I I 11.350 I I I I F17 - F17 .110*I I F5 - F5 .013 I I 8.789 I I I I F18 - F18 .251*I I F5 - F5 .023 I I 11.140 I I I I F11 - F11 .206*I I F10 - F10 .020 I I 10.472 I

VARIANCES OF INDEPENDENT VARIABLES (CONTINUED) ---------------------------------------------- E80 - Q51 .367*I I .025 I I 14.709 I I I I

E81 - Q52 .333*I I .017 I I 20.058 I I I I

E82 - Q53 .293*I I .015 I I 19.413 I I I I

E83 - Q54 .375*I I .018 I I 20.335 I I I I

E84 - Q55 .404*I I .020 I I 20.085 I I I I

E85 - Q56 .254*I I .014 I I 18.680 I I I I

E86 - Q57 .190*I I .011 I I 17.186 I I I I

E87 - Q58 .376*I I .019 I I 19.317 I I I I

E88 - Q59 .370*I I .018 I I 20.536 I I I I

E89 -Q12AB_CO .090*I .017 I I 5.248 TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 22 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAX COVARIANCES AMONG INDEPENDENT VARIABLES ----- I F5 - F5 .206*I I F4 - F4 .019 I I F10 - F10 I F4 - F4 .024 I I 8.770 I I I I F11 - F11 .142*I I F4 - F4 .014 I I 9.818 I I I I F12 - F12 .250*I I F4 - F4 .023 I I 10.878 I I I I F17 - F17 .099*I I F4 - F4 .013 I I 7.907 I I I I F18 - F18 .233*I I F4 - F4 .021 I I 11.239 I I I I F10 - F10 .227*I I F5 - F5 .024 I I 9.636 I I I I F11 - F11 .163*I I F5 - F5 .016 I I 10.242 I I I I F12 - F12 .295*I

178

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS 05/11/03 PAGE : 23 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) COVARIANCES AMONG INDEPENDENT VARIABLES (CONTINUED)

--------

S 05/11/03 PAGE : 24 Licensee: Mikhail Koulikov

ORY)

ARED

.771 .269

.389 .433

.568 .610

.474 .604

.683 .688

.739 .656 .682

.723 .373 .632

.584 .620

.617 .552 .640

.641 .353 .662

.619 .560 .663

.567 .598

.713 .780 .670

.538 .841

------------------------------------------- I F12 - F12 .409*I I F10 - F10 .032 I I 12.665 I I I I F17 - F17 .197*I I F10 - F10 .019 I I 10.534 I I I I F18 - F18 .365*I I F10 - F10 .029 I

I 12.644 I I I I F12 - F12 .308*I

I F11 - F11 .023 I I 13.373 I I I I F17 - F17 .100*I I F11 - F11 .011 I I 9.405 I I I I F18 - F18 .223*I I F11 - F11 .018 I I 12.184 I I I I F17 - F17 .182*I I F12 - F12 .017 I

I 10.518 I I I I F18 - F18 .452*I

I F12 - F12 .030 I I 15.090 I I I

I F18 - F18 .212*I I F17 - F17 .018 I I 12.116 I I I TITLE: Model created by EQS 5.7b -- 5F389I34.ED EQS/EM386 MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THE STANDARDIZED SOLUTION: R-SQU Q12C =V16 = .878*F4 + .478 E16 Q13 =V17 = .518 F5 + .855 E17 Q14A =V18 = .623*F5 + .782 E18 Q14B =V19 = .658*F5 + .753 E19 Q14C =V20 = .753*F5 + .658 E20 Q14D =V21 = .781*F5 + .625 E21 Q14E =V22 = .689*F5 + .725 E22 Q31A =V40 = .777 F10 + .629 E40 Q31B =V41 = .827*F10 + .563 E41

Q31C =V42 = .830*F10 + .558 E42 Q31D =V43 = .860*F10 + .510 E43 Q31E =V44 = .810*F10 + .586 E44

Q31F =V45 = .826*F10 + .564 E45 Q31G =V46 = .850*F10 + .526 E46 Q32A =V47 = .610 F11 + .792 E47

Q32B =V48 = .795*F11 + .607 E48 Q32C =V49 = .764*F11 + .645 E49 Q32D =V50 = .787*F11 + .617 E50 Q33 =V51 = .786 F12 + .619 E51 Q34 =V52 = .743*F12 + .669 E52 Q35 =V53 = .800*F12 + .600 E53 Q36 =V54 = .801*F12 + .599 E54 Q49 =V78 = .594 F17 + .804 E78 Q50 =V79 = .814*F17 + .581 E79 Q51 =V80 = .787*F17 + .617 E80 Q52 =V81 = .371*F17 + .453*F18 + .663 E81 Q53 =V82 = .814 F18 + .581 E82 Q54 =V83 = .753*F18 + .658 E83

Q55 =V84 = .773*F18 + .634 E84 Q56 =V85 = .844*F18 + .536 E85 Q57 =V86 = .883*F18 + .469 E86

Q58 =V87 = .819*F18 + .574 E87 Q59 =V88 = .733*F18 + .680 E88

_CO=V89 = .917 F4 + .399 E89 Q12AB

179


N (NORMAL DISTRIBUTION THEORY)

IABLES

05/11/03 PAGE : 26

ION IN GROUP 1

STRIBUTION THEORY)

ABLES (CONTINUED)

---------

---------

EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1 MAXIMUM LIKELIHOOD SOLUTIO CORRELATIONS AMONG INDEPENDENT VAR --------------------------------------- V F --- --- I F5 - F5 .544*I

I F4 - F4 I I I I F10 - F10 .331*I

I F4 - F4 I I I

I F11 - F11 .430*I I F4 - F4 I I I I F12 - F12 .446*I I F4 - F4 I I I I F17 - F17 .332*I I F4 - F4 I I I I F18 - F18 .444*I I F4 - F4 I I I I F10 - F10 .460*I I F5 - F5 I I I I F11 - F11 .624*I I F5 - F5 I I I I F12 - F12 .665*I

I F5 - F5 I I I

66*I I F17 - F17 .4 I F5 - F5 I I I I F18 - F18 .605*I

I I F5 - F5 I I

I F11 - F11 .478*I I F10 - F10 I

I I I F12 - F12 .560*I I F10 - F10 I I I

I F17 - F17 .503*I I F10 - F10 I I I

I F18 - F18 .532*I I F10 - F10 I

I I I F12 - F12 .796*I

I F11 - F11 I I I

TITLE: Model created by EQS 5.7b -- 5F389I34.EDS EQS/EM386 Licensee: Mikhail Koulikov

MAT MULTIPLE POPULATION ANALYSIS, INFOR

I MAXIMUM LIKELIHOOD SOLUTION (NORMAL D

RI CORRELATIONS AMONG INDEPENDENT VA ---------------------------------------------------

I F17 - F17 .482*I I F11 - F11 I

I I I F18 - F18 .615*I

I F11 - F11 I I I I F17 - F17 .519*I I F12 - F12 I I I

I F18 - F18 .736*I I F12 - F12 I I I

I F18 - F18 .647*I I F17 - F17 I

I I ---------------------------------------------------------------------- E N D O F M E T H O D

------ ----------------------------------------------------------------

180


NTERED DURING OPTIMIZATION.

Y IMPOSED

OMITTED]

MP~1\5F389I34.EDS 05/11/03 PAGE : 28

ION IN GROUP 2

STRIBUTION THEORY)

Q14C

0.086 7 0.110

012 0.031 0.030 -0.013

0.042 036 0.017

0.080 0.062

0.050 078 0.052

0.008 -0.090

4 -0.042 06 -0.029

0.001 0.109

-0.061 03 0.030

-0.075 -0.033

.025 0.012

6 0.048 -0.064 -0.071 -0.055 2 0.043 -0.074 -0.035 -0.083

.056 -0.060 _CO V 89 0.006 0.143 0.026 -0.028 0.095

Q31G V 46 0.005 -0.010 -0.028 -0.059 -0.038

Q32C V 49 -0.083 -0.030 -0.008 -0.028 -0.037

EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 2 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) PARAMETER ESTIMATES APPEAR IN ORDER, NO SPECIAL PROBLEMS WERE ENCOU ALL EQUALITY CONSTRAINTS WERE CORRECTL RESIDUAL COVARIANCE MATRIX (S-SIGMA) [ TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_I EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMAT MAXIMUM LIKELIHOOD SOLUTION (NORMAL DI STANDARDIZED RESIDUAL MATRIX: Q12C Q13 Q14A Q14B V 16 V 17 V 18 V 19 V 20 Q12C V 16 0.032 Q13 V 17 0.088 -0.020 Q14A V 18 0.063 -0.059 -0.227 Q14B V 19 -0.022 -0.069 0.073 0.025 Q14C V 20 0.056 -0.005 -0.006 0.062 Q14D V 21 -0.024 -0.069 -0.024 0.01 Q14E V 22 0.015 -0.099 -0.010 -0. Q31A V 40 0.054 0.195 0.033 0.045 Q31B V 41 0.064 0.143 -0.077 0.009 Q31C V 42 0.085 0.143 -0.036 0.006 Q31D V 43 0.071 0.140 -0.048 -0. Q31E V 44 0.113 0.172 0.039 0.029 Q31F V 45 0.089 0.151 -0.057 -0.012 Q31G V 46 0.065 0.226 -0.014 0.016 Q32A V 47 0.118 0.060 0.060 0. Q32B V 48 0.015 -0.009 0.041 0.016 Q32C V 49 -0.032 0.035 0.062 -0.114 Q32D V 50 0.021 -0.028 -0.019 -0.04 Q33 V 51 0.038 0.101 -0.046 0.0 Q34 V 52 0.039 0.157 -0.019 -0.052 Q35 V 53 -0.012 0.075 -0.049 0.037 - Q36 V 54 -0.016 0.085 0.013 0.031 Q49 V 78 0.099 0.044 0.034 -0.0 Q50 V 79 0.003 0.135 0.002 0.024 0.013 Q51 V 80 -0.052 0.071 0.020 -0.018 -0.039 Q52 V 81 -0.022 0.048 -0.134 Q53 V 82 0.011 0.022 -0.098 -0.029 -0.030 Q54 V 83 0.000 0.110 -0.019 -0 Q55 V 84 -0.070 0.042 -0.009 -0.032 -0.016 Q56 V 85 -0.07 Q57 V 86 -0.07 Q58 V 87 -0.061 0.090 -0.067 -0.072 -0.118 Q59 V 88 0.069 0.076 -0.019 -0 Q12AB Q14D Q14E Q31A Q31B Q31C V 21 V 22 V 40 V 41 V 42 Q14D V 21 0.043 Q14E V 22 0.043 0.006 Q31A V 40 0.012 -0.030 -0.022 Q31B V 41 -0.022 -0.019 0.043 -0.027 Q31C V 42 0.035 0.041 -0.013 -0.009 -0.034 Q31D V 43 0.038 0.020 -0.067 -0.090 0.006 Q31E V 44 0.064 0.023 -0.024 -0.044 -0.045 Q31F V 45 0.040 0.018 -0.067 -0.037 -0.030

Q32A V 47 0.025 0.096 0.026 0.019 0.057 Q32B V 48 -0.026 0.018 -0.047 -0.045 0.002

Q32D V 50 -0.072 0.040 -0.009 0.008 0.027 Q33 V 51 -0.093 -0.007 0.009 -0.006 0.031 Q34 V 52 -0.053 -0.015 0.004 0.037 0.034 Q35 V 53 -0.150 -0.078 0.007 -0.038 -0.056 Q36 V 54 -0.123 -0.048 0.017 -0.040 -0.055 Q49 V 78 -0.037 0.000 -0.060 -0.145 -0.074 Q50 V 79 -0.011 -0.055 -0.047 -0.037 -0.108 Q51 V 80 -0.142 -0.061 -0.026 -0.084 -0.116 Q52 V 81 -0.105 -0.074 -0.009 -0.095 -0.117 Q53 V 82 -0.109 -0.049 -0.015 -0.043 -0.062 Q54 V 83 -0.059 -0.049 0.050 0.023 -0.012 Q55 V 84 -0.116 -0.075 -0.026 -0.063 -0.022 Q56 V 85 -0.116 -0.104 -0.045 -0.085 -0.072 Q57 V 86 -0.116 -0.071 -0.065 -0.103 -0.106 Q58 V 87 -0.118 -0.109 0.066 0.057 0.006 Q59 V 88 -0.067 -0.064 0.001 0.012 -0.015 Q12AB_CO V 89 0.026 0.053 0.044 0.034 0.069

181

Q31D Q31E Q31F Q31G Q32A

V 45 -0.027 0.013 -0.040

61 0.107 0.068 0.112 0.033

2 0.038 0.000 0.071 02 0.042 0.041 0.076 0.054

0.067 0.050 0.058 0.096 0.072 0.002

-0.020 0.056

0.049 0.053

66 -0.022 0.043

0.042 23 0.009

0.018 0.064

61 -0.077 .032

034 0.088

4 52

0.098 -0.126

63 -0.151 0.067

-0.050 12 -0.057

0.175 -0.101

0.114 -0.128

58 -0.144 0.108

-0.069 0.063 0.091

1 V 80

-0.146 56 -0.203

0.070 0.044

9 -0.120 -0.009 0.015 -0.099

9 0.037 -0.032

_CO V 89 -0.044 -0.025 0.084 0.019 -0.035

Q56

Q53 V 82 -0.128 -0.094

60 -0.048 0.010

-0.134 99 -0.038

0.005

O

Q59 V 88 -0.036 -0.064 0.017 049

ABSOLUTE STANDARDIZED RESIDUALS = 0.0596 FF-DIAGONAL ABSOLUTE STANDARDIZED RESIDUALS = 0.0585

V 43 V 44 V 45 V 46 V 47 Q31D V 43 -0.091 Q31E V 44 -0.004 -0.001 Q31F Q31G V 46 -0.092 -0.025 -0.029 -0.009 Q32A V 47 0.0 Q32B V 48 0.007 0.095 0.042 0.040 0.127 Q32C V 49 -0.005 0.04 Q32D V 50 -0.0 Q33 V 51 0.021 0.060 Q34 V 52 0.049 0.084 Q35 V 53 -0.101 -0.006 -0.001 0.027 Q36 V 54 -0.052 0.019 -0.003 0.013 Q49 V 78 -0.041 0.034 -0.023 -0.046 0.065 Q50 V 79 -0.059 -0.038 -0.017 -0.037 Q51 V 80 -0.096 -0.014 -0.054 -0.001 Q52 V 81 -0.069 -0.079 -0.085 -0.0 Q53 V 82 -0.092 -0.010 0.002 -0.017 Q54 V 83 -0.024 0.069 0.076 0.042 Q55 V 84 -0.059 -0.056 0.010 0.0 Q56 V 85 -0.112 -0.068 -0.005 -0.074 Q57 V 86 -0.133 -0.059 -0.028 -0.064 Q58 V 87 -0.019 0.035 0.035 0.0 Q59 V 88 -0.017 0.082 0.063 0.005 0 Q12AB_CO V 89 0.056 0.055 0.082 0. Q32B Q32C Q32D Q33 Q3 V 48 V 49 V 50 V 51 V Q32B V 48 -0.009 Q32C V 49 -0.086 -0.196 Q32D V 50 -0.056 -0.134 -0.061 Q33 V 51 -0.001 -0.067 0.052 -0.073 Q34 V 52 -0.018 -0.042 -0.069 0.032 - Q35 V 53 -0.084 -0.163 -0.061 -0.097 Q36 V 54 -0.005 -0.077 -0.035 -0.1 Q49 V 78 -0.008 -0.034 0.033 -0.022 - Q50 V 79 0.017 -0.007 -0.028 -0.004 Q51 V 80 -0.047 -0.053 -0.038 -0.0 Q52 V 81 -0.164 -0.152 -0.123 -0.150 - Q53 V 82 -0.065 -0.122 -0.074 -0.153 Q54 V 83 -0.046 -0.021 0.030 -0.066 -0.060 Q55 V 84 -0.038 -0.051 -0.062 -0.101 - Q56 V 85 -0.096 -0.071 -0.092 -0.157 Q57 V 86 -0.073 -0.103 -0.093 -0.1 Q58 V 87 -0.146 -0.125 -0.061 -0.095 - Q59 V 88 -0.064 -0.041 -0.093 -0.113 Q12AB_CO V 89 0.030 -0.021 0.009 Q35 Q36 Q49 Q50 Q5 V 53 V 54 V 78 V 79 Q35 V 53 -0.150 Q36 V 54 -0.052 -0.109 Q49 V 78 -0.016 -0.021 -0.088 Q50 V 79 -0.002 0.039 -0.108 -0.034 Q51 V 80 -0.008 0.006 -0.067 -0.100 Q52 V 81 -0.204 -0.145 -0.093 -0.1 Q53 V 82 -0.122 -0.112 -0.030 -0.053 - Q54 V 83 -0.067 -0.042 0.023 0.089 Q55 V 84 -0.132 -0.095 -0.045 0.049 -0.032 Q56 V 85 -0.14 Q57 V 86 -0.120 -0.116 -0.003 -0.049 -0.092 Q58 V 87 -0.031 -0.090 -0.07 Q59 V 88 -0.066 -0.043 -0.004 0.056 -0.043 Q12AB Q52 Q53 Q54 Q55 V 81 V 82 V 83 V 84 V 85 Q52 V 81 -0.233 Q54 V 83 -0.121 -0.057 -0.157 Q55 V 84 -0.198 -0.132 -0.081 -0.206 Q56 V 85 -0.186 -0.098 -0.057 -0.0 Q57 V 86 -0.174 -0.098 -0.103 -0.157 Q58 V 87 -0.150 -0.184 -0.093 -0.176 Q59 V 88 -0.112 -0.047 -0.067 -0.0 Q12AB_CO V 89 0.013 0.044 0.041 -0.023 Q57 Q58 Q59 Q12AB_C V 86 V 87 V 88 V 89 Q57 V 86 -0.071 Q58 V 87 -0.166 -0.121

Q12AB_CO V 89 -0.031 -0.052 0.066 0. AVERAGE AVERAGE O

182

LARGEST STANDARDIZED RESIDUALS:

8 V 46,V 17 V 84,V 84 V 81,V 53

0.196 0.195 -0.186

0.175 -0.174 0.172

0.163 -0.163 -0.158

HESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 29

N IN GROUP 2

TION (NORMAL DISTRIBUTION THEORY)

15 RESIDUALS

V 81,V 81 V 18,V 1 -0.233 -0.227 0.226 -0.206 -0.204 V 81,V 80 V 84,V 81 V 49,V 49 V 40,V 17 V 85,V 81 -0.203 -0.198 - V 87,V 82 V 87,V 84 V 81,V 52 V 86,V 81 V 44,V 17 -0.184 -0.176 - V 87,V 86 V 81,V 48 V 54,V 51 V 53,V 49 V 86,V 51 -0.166 -0.164 - TITLE: Model created by EQS 5.7b -- C:\T EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATIO MAXIMUM LIKELIHOOD SOLU DISTRIBUTION OF STANDARDIZED RESIDUALS ---------------------------------------- ! ! 300- - ! * ! ! * ! ! * ! ! * ! RANGE FREQ PERCENT 225- * - ! * * ! 1 -0.5 - -- 0 0.00% ! * * ! 2 -0.4 - -0.5 0 0.00% ! * * ! 3 -0.3 - -0.4 0 0.00% ! * * ! 4 -0.2 - -0.3 5 0.84% 150- * * - 5 -0.1 - -0.2 79 13.28% ! * * ! 6 0.0 - -0.1 290 48.74% ! * * ! 7 0.1 - 0.0 203 34.12% ! * * ! 8 0.2 - 0.1 17 2.86% ! * * ! 9 0.3 - 0.2 1 0.17% 75- * * * - A 0.4 - 0.3 0 0.00% ! * * * ! B 0.5 - 0.4 0 0.00% ! * * * ! C ++ - 0.5 0 0.00% ! * * * ! ------------------------------- ! * * * * ! TOTAL 595 100.00% ---------------------------------------- 1 2 3 4 5 6 7 8 9 A B C EACH "*" REPRESENTS

183



RMAL DISTRIBUTION THEORY) RRORS AND TEST STATISTICS

.084

F5 + 1.000 E22

+ 1.000 E40

.034

AND TEST STATISTICS (CONTINUED)

ITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 31

N IN GROUP 2

TION (NORMAL DISTRIBUTION THEORY)

.037 28.438

Q32A =V47 = 1.000 F11 + 1.000 E47

18.847

Q50 =V79 = 1.736*F17 + 1.000 E79 .098 17.802 MEASUREMENT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS (CONTINUED)

EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION MAXIMUM LIKELIHOOD SOLUTION (NO MEASUREMENT EQUATIONS WITH STANDARD E Q12C =V16 = 1.056*F4 + 1.000 E16 .043 24.535

Q13 =V17 = 1.000 F5 + 1.000 E17

Q14A =V18 = 1.131*F5 + 1.000 E18 .081 13.939

Q14B =V19 = 1.242*F5 + 1.000 E19 .086 14.375

Q14C =V20 = 1.254*F5 + 1.000 E20 .081 15.407

Q14D =V21 = 1.308*F5 + 1.000 E21

15.659

Q14E =V22 = 1.203* .082 14.729

Q31A =V40 = 1.000 F10

Q31B =V41 = 1.016*F10 + 1.000 E41 .036 28.471

Q31C =V42 = 1.013*F10 + 1.000 E42 .035 28.605

Q31D =V43 = 1.053*F10 + 1.000 E43 .035 29.952

Q31E =V44 = .946*F10 + 1.000 E44

27.750 MEASUREMENT EQUATIONS WITH STANDARD ERRORS T EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATIO MAXIMUM LIKELIHOOD SOLU Q31F =V45 = 1.054*F10 + 1.000 E45

Q31G =V46 = 1.095*F10 + 1.000 E46 .037 29.515

Q32B =V48 = 1.380*F11 + 1.000 E48 .073 18.956

Q32C =V49 = 1.246*F11 + 1.000 E49 .067

18.498

Q32D =V50 = 1.369*F11 + 1.000 E50 .073

Q33 =V51 = 1.000 F12 + 1.000 E51

Q34 =V52 = .880*F12 + 1.000 E52 .036 24.250

Q35 =V53 = 1.048*F12 + 1.000 E53 .040 26.485

Q36 =V54 = .966*F12 + 1.000 E54 .036 26.499

Q49 =V78 = 1.000 F17 + 1.000 E78

184


Licensee: Mikhail Koulikov GROUP 2


~1\5F389I34.EDS 05/11/03 PAGE : 33

IN GROUP 2

TION THEORY)

---------------------------------- V F --- --- I F4 - F4 .479*I I .030 I I 15.747 I I I I F5 - F5 .299*I I .037 I I 8.171 I I I I F10 - F10 .816*I I .057 I I 14.268 I I I I F11 - F11 .228*I I .023 I I 9.965 I I I I F12 - F12 .656*I I .046 I I 14.184 I I I I F17 - F17 .187*I I .020 I I 9.429 I I I I F18 - F18 .576*I I .038 I I 15.299 I I I

EQS/EM386 MULTIPLE POPULATION ANALYSIS, INFORMATION IN MAX Q51 =V80 = 1.785*F17 + 1.000 E80 .102 17.572 Q52 =V81 = .745*F17 + .519*F18 + 1.000 E81 .079 .040 9.416 12.964 Q53 =V82 = 1.000 F18 + 1.000 E82 Q54 =V83 = .922*F18 + 1.000 E83 .034 26.736 Q55 =V84 = 1.021*F18 + 1.000 E84 .037 27.741 Q56 =V85 = 1.047*F18 + 1.000 E85 .033 31.496 Q57 =V86 = 1.082*F18 + 1.000 E86 .032 33.713 Q58 =V87 = 1.151*F18 + 1.000 E87 .038 30.095 Q59 =V88 = .865*F18 + 1.000 E88 .033 25.810 Q12AB_CO=V89 = 1.000 F4 + 1.000 E89 TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBU VARIANCES OF INDEPENDENT VARIABLES

185

TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 34 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 2 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) VARIANCES OF INDEPENDENT VARIABLES E D --- --- E16 - Q12C .158*I I

20.016 I I


NFORMATION IN GROUP 2


--------------------------------------------

.020 I I 7.915 I I I I

E17 - Q13 .815*I I .039 I I 20.918 I I I I

E18 - Q14A .602*I I .030 I I 20.009 I I I I

E19 - Q14B .604*I I .031 I I 19.570 I I I I

E20 - Q14C .359*I I .020 I I 17.689 I I I I

E21 - Q14D .328*I I .019 I I 16.837 I I I I

E22 - Q14E .480*I I .025 I I 19.102 I I I I

E40 - Q31A .535*I I .027 I I I I

E41 - Q31B .390*I I .020 I I 19.119 I I I I

E42 - Q31C .379*I I .020 I I 19.047 I I I I

E43 - Q31D .318*I I .018 I I 18.155 I I I I

E44 - Q31E .382*I I .020 I I 19.471 I I T EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, I MAX VARIANCES OF INDEPENDENT VARIABLES (CONTINUED) -- E45 - Q31F .422*I I .022 I I 19.136 I I I I

E46 - Q31G .375*I I .020 I I 18.481 I I I I

E47 - Q32A .384*I I .019 I I 20.199 I I I I

E48 - Q32B .254*I I .015 I I 16.400 I I I I

E49 - Q32C .252*I I .014 I I 17.455 I I I I

E50 - Q32D .263*I I .016 I I 16.686 I I I I

E51 - Q33 .407*I I .023 I I 17.991 I I I I

E52 - Q34 .412*I I .022 I I 18.988 I I I I

E53 - Q35 .405*I I .023 I I 17.546 I I I I

E54 - Q36 .343*I I .020 I I 17.535 I I I I

E78 - Q49 .344*I I .017 I I 19.793 I I I I

E79 - Q50 .288*I I .022 I I 13.269 I I I I

186


NFORMATION IN GROUP 2


--------------------------------------------

81 - Q52 .333*I I

I I

86 - Q57 .190*I I

.019 I I

389I34.EDS 05/11/03 PAGE : 37

ION IN GROUP 2

THEORY) RIABLES

--- ---

I 8.770 I I I

I

I 9.818 I .250*I .023 I

EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, I MAX VARIANCES OF INDEPENDENT VARIABLES (CONTINUED) --E80 - Q51 .367*I I .025 I I 14.709 I I

I I

E .017 I I 20.058 I I I I

82 - Q53 .293*I I E .015 I I 19.413 I I I I

E83 - Q54 .375*I I .018 I I

20.335 I I I I

E84 - Q55 .404*I I .020 I I

20.085 I I

E85 - Q56 .254*I I .014 I I 18.680 I I

I I

E .011 I I 17.186 I I I I

E87 - Q58 .376*I I 19.317 I I I I

E88 - Q59 .370*I I .018 I I 20.536 I I I I

I E89 -Q12AB_CO .090*I .017 I I

5.248 I I

\SP_IMP~1\5F TITLE: Model created by EQS 5.7b -- C:\THESIS EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMAT

AL DISTRIBUTION MAXIMUM LIKELIHOOD SOLUTION (NORM

COVARIANCES AMONG INDEPENDENT VA V F

I F5 - F5 .206*I 9 I I F4 - F4 .01

I 10.627 I I I

I F10 - F10 .207*I I F4 - F4 .024 I

I F11 - F11 .142*I I F4 - F4 .014 I I

I F12 - F12 I F4 - F4 I 10.878 I I I

I F17 - F17 .099*I I F4 - F4 .013 I I 7.907 I I I

I F18 - F18 .233*I I F4 - F4 .021 I

I 11.239 I I I

I F10 - F10 .227*I I F5 - F5 .024 I

I 9.636 I I I

I F11 - F11 .163*I I F5 - F5 .016 I

I 10.242 I I I

I F12 - F12 .295*I I F5 - F5 .026 I I 11.350 I I I

I F17 - F17 .110*I I F5 - F5 .013 I I 8.789 I I I

I F18 - F18 .251*I I F5 - F5 .023 I I 11.140 I I I

I F11 - F11 .206*I I F10 - F10 .020 I

I 10.472 I I I

187

TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 38 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 2 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) COVARIANCES AMONG INDEPENDENT VARIABLES (CONTINUED)

-------- .409*I

SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 39

IN GROUP 2


R-SQUARED

.604 .683

.688 .739

.656 .682 .723

.373 .632

.584 .620

.617 .552

.640 .641

.353 .662

.619 .560

.663 .567

.598 .713 .780

.670 .538

.841

------------------------------------------- I F12 - F12 I F10 - F10 .032 I I 12.665 I I I

I F17 - F17 .197*I I F10 - F10 .019 I I 10.534 I I I

I F18 - F18 .365*I I F10 - F10 .029 I I 12.644 I I I

I F12 - F12 .308*I I F11 - F11 .023 I I 13.373 I I I

I F17 - F17 .100*I I F11 - F11 .011 I I 9.405 I I I

I F18 - F18 .223*I I F11 - F11 .018 I I 12.184 I I I

I F17 - F17 .182*I I F12 - F12 .017 I I 10.518 I I I

I F18 - F18 .452*I I F12 - F12 .030 I I 15.090 I I I

I F18 - F18 .212*I I F17 - F17 .018 I I 12.116 I TITLE: Model created by EQS 5.7b -- C:\THESIS\ EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION MAXIMUM LIKELIHOOD SOLUTION (NORMAL STANDARDIZED SOLUTION: Q12C =V16 = .878*F4 + .478 E16 .771

.269 Q13 =V17 = .518 F5 + .855 E17 Q14A =V18 = .623*F5 + .782 E18 .389

=V19 = .658*F5 + .753 E19 .433 Q14B Q14C =V20 = .753*F5 + .658 E20 .568 Q14D =V21 = .781*F5 + .625 E21 .610

.474 Q14E =V22 = .689*F5 + .725 E22 Q31A =V40 = .777 F10 + .629 E40

Q31B =V41 = .827*F10 + .563 E41 Q31C =V42 = .830*F10 + .558 E42

=V43 = .860*F10 + .510 E43 Q31D Q31E =V44 = .810*F10 + .586 E44 Q31F =V45 = .826*F10 + .564 E45 Q31G =V46 = .850*F10 + .526 E46 Q32A =V47 = .610 F11 + .792 E47 Q32B =V48 = .795*F11 + .607 E48 Q32C =V49 = .764*F11 + .645 E49 Q32D =V50 = .787*F11 + .617 E50 Q33 =V51 = .786 F12 + .619 E51 Q34 =V52 = .743*F12 + .669 E52 Q35 =V53 = .800*F12 + .600 E53 Q36 =V54 = .801*F12 + .599 E54 Q49 =V78 = .594 F17 + .804 E78 Q50 =V79 = .814*F17 + .581 E79 Q51 =V80 = .787*F17 + .617 E80 Q52 =V81 = .371*F17 + .453*F18 + .663 E81 Q53 =V82 = .814 F18 + .581 E82

Q54 =V83 = .753*F18 + .658 E83 Q55 =V84 = .773*F18 + .634 E84 Q56 =V85 = .844*F18 + .536 E85 Q57 =V86 = .883*F18 + .469 E86 Q58 =V87 = .819*F18 + .574 E87 Q59 =V88 = .733*F18 + .680 E88 Q12AB_CO=V89 = .917 F4 + .399 E89

188

TITLE: Model created by EQS 5.7b -- C:\THESIS\SP_IMP~1\5F389I34.EDS 05/11/03 PAGE : 40 EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 2 MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY) CORRELATIONS AMONG INDEPENDENT VARIABLES V F I F5 - F5 .544*I I F4 - F4 I I I

I F10 - F10 .331*I

I F10 - F10 I

I F11 - F11 I


N GROUP 2

TION (NORMAL DISTRIBUTION THEORY) LES (CONTINUED)

--------

------------

YSIS TLY IMPOSED

30 ON 1122 DEGREES OF FREEDOM NCE CAIC = 14190.86645

= -5826.69891

ES OF FREEDOM ATISTIC IS LESS THAN 0.001

6

0.918

ITERATION ABS CHANGE ALPHA FUNCTION 1 0.063125 1.00000 2.96883 2 0.011439 1.00000 2.90986 3 0.002359 1.00000 2.90826 4 0.000429 1.00000 2.90823

I F4 - F4 I I I

I F11 - F11 .430*I I F4 - F4 I I I

I F12 - F12 .446*I I F4 - F4 I I I

I F17 - F17 .332*I I F4 - F4 I I I

I F18 - F18 .444*I I F4 - F4 I I I

I F10 - F10 .460*I I F5 - F5 I I I

I F11 - F11 .624*I I F5 - F5 I

I I

I F12 - F12 .665*I I F5 - F5 I I I

I F17 - F17 .466*I I F5 - F5 I I I

I F18 - F18 .605*I I F5 - F5 I

I I

I F11 - F11 .478*I I F10 - F10 I I I

I F12 - F12 .560*I I F10 - F10 I I I

I F17 - F17 .503*I I F10 - F10 I

I I

I F18 - F18 .532*I I I

I F12 - F12 .796*I

T EQS/EM386 Licensee: Mikhail Koulikov MULTIPLE POPULATION ANALYSIS, INFORMATION I MAXIMUM LIKELIHOOD SOLU CORRELATIONS AMONG INDEPENDENT VARIAB --------------------------------------------------- I F17 - F17 .482*I I F11 - F11 I I I I F18 - F18 .615*I I F11 - F11 I I I I F17 - F17 .519*I I F12 - F12 I I I

I F18 - F18 .736*I I F12 - F12 I

I I I F18 - F18 .647*I I F17 - F17 I ----------------------------------------------------------------------- E N D O F M E T H O D ------------------------------------------------------------------- STATISTICS FOR MULTIPLE POPULATION ANAL ALL EQUALITY CONSTRAINTS WERE CORREC GOODNESS OF FIT SUMMARY INDEPENDENCE MODEL CHI-SQUARE = 23044.1 INDEPENDENCE AIC = 20800.12988 INDEPENDE MODEL AIC = 652.97112 MODEL CAIC

RE CHI-SQUARE = 2852.971 BASED ON 1100 DEGST PROBABILITY VALUE FOR THE CHI-SQUARE

BENTLER-BONETT NORMED FIT INDEX= 0.87 BENTLER-BONETT NONNORMED FIT INDEX= COMPARATIVE FIT INDEX (CFI) = 0.920 ITERATIVE SUMMARY PARAMETER

189

TITLE: 05/11/03 PAGE : 42 EQS/EM386 Licensee: Mikhail Koulikov ELEASING CONSTRAINTS) CONSTRAINTS TO BE RELEASED ARE:

ONSTR: 1 (1,F4,F4)-(2,F4,F4)=0;

1)=0;

5)-(2,V22,F5)=0;

10)=0;

10)-(2,V44,F10)=0;

10)-(2,V46,F10)=0; 11)-(2,V48,F11)=0;

CONSTR: 78 (1,E82,E82)-(2,E82,E82)=0; CONSTR: 79 (1,E83,E83)-(2,E83,E83)=0; CONSTR: 80 (1,E84,E84)-(2,E84,E84)=0; CONSTR: 81 (1,E85,E85)-(2,E85,E85)=0; CONSTR: 82 (1,E86,E86)-(2,E86,E86)=0; CONSTR: 83 (1,E87,E87)-(2,E87,E87)=0; CONSTR: 84 (1,E88,E88)-(2,E88,E88)=0; CONSTR: 85 (1,V83,F18)-(2,V83,F18)=0; CONSTR: 86 (1,V84,F18)-(2,V84,F18)=0; CONSTR: 87 (1,V85,F18)-(2,V85,F18)=0; CONSTR: 88 (1,V86,F18)-(2,V86,F18)=0; CONSTR: 89 (1,V87,F18)-(2,V87,F18)=0; CONSTR: 90 (1,V88,F18)-(2,V88,F18)=0;

LAGRANGE MULTIPLIER TEST (FOR R CONSTRAINTS FROM GROUP 2 C CONSTR: 2 (1,F5,F5)-(2,F5,F5)=0; CONSTR: 3 (1,F10,F10)-(2,F10,F10)=0; CONSTR: 4 (1,F11,F11)-(2,F11,F1 CONSTR: 5 (1,F12,F12)-(2,F12,F12)=0; CONSTR: 6 (1,F17,F17)-(2,F17,F17)=0; CONSTR: 7 (1,F18,F18)-(2,F18,F18)=0; CONSTR: 8 (1,F4,F5)-(2,F4,F5)=0; CONSTR: 9 (1,F4,F10)-(2,F4,F10)=0; CONSTR: 10 (1,F4,F11)-(2,F4,F11)=0; CONSTR: 11 (1,F4,F12)-(2,F4,F12)=0; CONSTR: 12 (1,F4,F17)-(2,F4,F17)=0; CONSTR: 13 (1,F4,F18)-(2,F4,F18)=0; CONSTR: 14 (1,F5,F10)-(2,F5,F10)=0; CONSTR: 15 (1,F5,F11)-(2,F5,F11)=0; CONSTR: 16 (1,F5,F12)-(2,F5,F12)=0;

TR: 17 (1,F5,F17)-(2,F5,F17)=0; CONS CONSTR: 18 (1,F5,F18)-(2,F5,F18)=0; CONSTR: 19 (1,F10,F11)-(2,F10,F11)=0; CONSTR: 20 (1,F10,F12)-(2,F10,F12)=0; CONSTR: 21 (1,F10,F17)-(2,F10,F17)=0; CONSTR: 22 (1,F10,F18)-(2,F10,F18)=0; CONSTR: 23 (1,F11,F12)-(2,F11,F12)=0; CONSTR: 24 (1,F11,F17)-(2,F11,F17)=0; CONSTR: 25 (1,F11,F18)-(2,F11,F18)=0; CONSTR: 26 (1,F12,F17)-(2,F12,F17)=0; CONSTR: 27 (1,F12,F18)-(2,F12,F18)=0; CONSTR: 28 (1,F17,F18)-(2,F17,F18)=0; CONSTR: 29 (1,V16,F4)-(2,V16,F4)=0; CONSTR: 30 (1,V18,F5)-(2,V18,F5)=0; CONSTR: 31 (1,V19,F5)-(2,V19,F5)=0; CONSTR: 32 (1,V20,F5)-(2,V20,F5)=0;

5)-(2,V21,F5)=0; CONSTR: 33 (1,V21,F CONSTR: 34 (1,V22,F CONSTR: 35 (1,V41,F10)-(2,V41,F10)=0; CONSTR: 36 (1,V42,F10)-(2,V42,F CONSTR: 37 (1,V43,F10)-(2,V43,F10)=0; CONSTR: 38 (1,V44,F CONSTR: 39 (1,V45,F10)-(2,V45,F10)=0; CONSTR: 40 (1,V46,F CONSTR: 41 (1,V48,F CONSTR: 42 (1,V49,F11)-(2,V49,F11)=0;

11)-(2,V50,F11)=0; CONSTR: 43 (1,V50,F CONSTR: 44 (1,V52,F12)-(2,V52,F12)=0; CONSTR: 45 (1,V53,F12)-(2,V53,F12)=0; CONSTR: 46 (1,V54,F12)-(2,V54,F12)=0; CONSTR: 47 (1,V79,F17)-(2,V79,F17)=0; CONSTR: 48 (1,V80,F17)-(2,V80,F17)=0; CONSTR: 49 (1,V81,F17)-(2,V81,F17)=0; CONSTR: 50 (1,V81,F18)-(2,V81,F18)=0; CONSTR: 51 (1,E89,E89)-(2,E89,E89)=0; CONSTR: 52 (1,E16,E16)-(2,E16,E16)=0; CONSTR: 53 (1,E17,E17)-(2,E17,E17)=0; CONSTR: 54 (1,E18,E18)-(2,E18,E18)=0; CONSTR: 55 (1,E19,E19)-(2,E19,E19)=0; CONSTR: 56 (1,E20,E20)-(2,E20,E20)=0; CONSTR: 57 (1,E21,E21)-(2,E21,E21)=0; CONSTR: 58 (1,E22,E22)-(2,E22,E22)=0; CONSTR: 59 (1,E40,E40)-(2,E40,E40)=0; CONSTR: 60 (1,E41,E41)-(2,E41,E41)=0; CONSTR: 61 (1,E42,E42)-(2,E42,E42)=0; CONSTR: 62 (1,E43,E43)-(2,E43,E43)=0; CONSTR: 63 (1,E44,E44)-(2,E44,E44)=0; CONSTR: 64 (1,E45,E45)-(2,E45,E45)=0; CONSTR: 65 (1,E46,E46)-(2,E46,E46)=0; CONSTR: 66 (1,E47,E47)-(2,E47,E47)=0; CONSTR: 67 (1,E48,E48)-(2,E48,E48)=0; CONSTR: 68 (1,E49,E49)-(2,E49,E49)=0; CONSTR: 69 (1,E50,E50)-(2,E50,E50)=0; CONSTR: 70 (1,E51,E51)-(2,E51,E51)=0; CONSTR: 71 (1,E52,E52)-(2,E52,E52)=0; CONSTR: 72 (1,E53,E53)-(2,E53,E53)=0; CONSTR: 73 (1,E54,E54)-(2,E54,E54)=0; CONSTR: 74 (1,E78,E78)-(2,E78,E78)=0; CONSTR: 75 (1,E79,E79)-(2,E79,E79)=0; CONSTR: 76 (1,E80,E80)-(2,E80,E80)=0; CONSTR: 77 (1,E81,E81)-(2,E81,E81)=0;

190

UNIVARIATE TEST STATISTICS: NO CONSTRAINT CHI-SQUARE PROBABILITY

0.653 0.419

045

025 665

7.032 0.008 90 CONSTR: 90 3.303 0.069

-- ----------- ---------- ----------- 1 CONSTR: 1 2 CONSTR: 2 3.380 0.066 3 CONSTR: 3 5.101 0.024 4 CONSTR: 4 4.015 0. 5 CONSTR: 5 4.568 0.033 6 CONSTR: 6 16.445 0.000 7 CONSTR: 7 0.076 0.783 8 CONSTR: 8 1.204 0.272 9 CONSTR: 9 2.963 0.085 10 CONSTR: 10 0.006 0.937 11 CONSTR: 11 0.728 0.394 12 CONSTR: 12 0.071 0.790

ONSTR: 13 0.063 0.802 13 C 14 CONSTR: 14 2.551 0.110 15 CONSTR: 15 0.009 0.925 16 CONSTR: 16 0.880 0.348 17 CONSTR: 17 0.000 0.984 18 CONSTR: 18 4.247 0.039 19 CONSTR: 19 0.732 0.392 20 CONSTR: 20 4.240 0.039 21 CONSTR: 21 4.733 0.030 22 CONSTR: 22 0.000 0.997 23 CONSTR: 23 5.851 0.016 24 CONSTR: 24 0.000 0.991 25 CONSTR: 25 0.198 0.657

ONSTR: 26 3.404 0.065 26 C 27 CONSTR: 27 6.183 0.013 28 CONSTR: 28 7.576 0.006 29 CONSTR: 29 0.558 0.455 30 CONSTR: 30 2.923 0.087 31 CONSTR: 31 0.289 0.591 32 CONSTR: 32 10.911 0.001 33 CONSTR: 33 0.755 0.385 34 CONSTR: 34 0.029 0.864 35 CONSTR: 35 0.229 0.632 36 CONSTR: 36 0.265 0.607 37 CONSTR: 37 2.793 0.095 38 CONSTR: 38 0.920 0.337

0.068 0.795 39 CONSTR: 39 40 CONSTR: 40 0.331 0.565 41 CONSTR: 41 0.916 0.339 42 CONSTR: 42 16.043 0.000

1.599 0.206 43 CONSTR: 43 44 CONSTR: 44 0.083 0.773 45 CONSTR: 45 2.949 0.086 46 CONSTR: 46 2.203 0.138 47 CONSTR: 47 1.002 0.317 48 CONSTR: 48 3.907 0.048 49 CONSTR: 49 29.581 0.000 50 CONSTR: 50 21.146 0.000 51 CONSTR: 51 11.371 0.001 52 CONSTR: 52 15.038 0.000 53 CONSTR: 53 0.291 0.590 54 CONSTR: 54 37.294 0.000 55 CONSTR: 55 0.082 0.774 56 CONSTR: 56 0.037 0.848 57 CONSTR: 57 1.554 0.213 58 CONSTR: 58 0.070 0.791 59 CONSTR: 59 0.011 0.918 60 CONSTR: 60 1.486 0.223 61 CONSTR: 61 2.166 0.141 62 CONSTR: 62 2.259 0.133 63 CONSTR: 63 0.007 0.934

1.955 0.162 64 CONSTR: 64 65 CONSTR: 65 12.447 0.000 66 CONSTR: 66 12.359 0.000

0.000 0.993 67 CONSTR: 67 68 CONSTR: 68 5.035 0.

0.188 0. 69 CONSTR: 69 70 CONSTR: 70 0.526 0.468 71 CONSTR: 71 2.055 0.152 72 CONSTR: 72 2.792 0.095

0.198 0.656 73 CONSTR: 73 74 CONSTR: 74 0.592 0.441 75 CONSTR: 75 4.336 0.037

0.670 0.413 76 CONSTR: 76 77 CONSTR: 77 1.536 0.215 78 CONSTR: 78 1.688 0.194 79 CONSTR: 79 21.129 0.000

6.274 0.012 80 CONSTR: 80 81 CONSTR: 81 0.557 0.455

87 0.062 82 CONSTR: 82 3.4 83 CONSTR: 83 12.611 0.000 84 CONSTR: 84 3.248 0.072 85 CONSTR: 85 0.202 0.653 86 CONSTR: 86 4.523 0.033 87 CONSTR: 87 4.320 0.038

0.576 0.448 88 CONSTR: 88 89 CONSTR: 89

191

CUMULATIVE MULTIVARIATE STATISTICS UNIVARIATE INCREMENT STEP PARAMETER CHI-SQUARE D.F. PROBABILITY CHI-SQUARE PROBABILITY 1 CONSTR: 54 37.294 1 0.000 37.294 0.000

29.582 0.000 21.152 0.000

16.154 0.000 15.047 0.000 12.949 0.000 12.946 0.000 12.445 0.000

12.033 0.001 11.817 0.001 10.864 0.001 10.263 0.001 9.609 0.002 7.403 0.007 5.920 0.015 5.457 0.019 12.428 0.000 5.421 0.020 5.521 0.019

8.953 0.003 5.249 0.022 4.575 0.032 4.119 0.042 4.021 0.045 3.494 0.062 5.399 0.020 6.054 0.014 3.897 0.048 3.754 0.053

4.792 0.029 3.369 0.066 2.879 0.090 2.823 0.093 3.321 0.068 2.557 0.110

2.711 0.100 2.519 0.112 2.420 0.120 2.416 0.120 2.216 0.137 2.115 0.146 2.041 0.153 2.036 0.154 1.919 0.166 1.608 0.205 1.591 0.207 1.541 0.215 1.510 0.219 1.552 0.213 1.652 0.199 1.895 0.169 1.726 0.189 1.502 0.220 1.840 0.175 1.469 0.226 1.466 0.226 1.475 0.224 1.218 0.270 1.245 0.265 1.198 0.274 1.151 0.283 1.106 0.293 1.282 0.257

0.830 0.362 1.055 0.304 0.611 0.434 0.871 0.351 0.799 0.371 0.533 0.465 0.468 0.494 0.398 0.528 0.374 0.541 0.287 0.592 0.273 0.601 0.327 0.567 0.782 0.376 0.194 0.659 0.143 0.705 0.125 0.724

0.093 0.761 0.104 0.747 0.083 0.773 0.065 0.798 0.051 0.821 0.033 0.857 0.029 0.866 0.018 0.894 0.011 0.917 0.002 0.961 0.001 0.977 22:50:35.16 Elapsed time = 8.74 seconds

2 CONSTR: 49 66.875 2 0.000 3 CONSTR: 79 88.027 3 0.000 4 CONSTR: 42 104.181 4 0.000 5 CONSTR: 52 119.228 5 0.000 6 CONSTR: 83 132.177 6 0.000 7 CONSTR: 66 145.123 7 0.000 8 CONSTR: 65 157.568 8 0.000 9 CONSTR: 89 169.601 9 0.000 10 CONSTR: 6 181.417 10 0.000 11 CONSTR: 21 192.282 11 0.000 12 CONSTR: 3 202.544 12 0.000 13 CONSTR: 32 212.154 13 0.000 14 CONSTR: 86 219.557 14 0.000 15 CONSTR: 75 225.477 15 0.000 16 CONSTR: 27 230.935 16 0.000 17 CONSTR: 5 243.363 17 0.000 18 CONSTR: 43 248.784 18 0.000 19 CONSTR: 16 254.304 19 0.000 20 CONSTR: 18 263.258 20 0.000 21 CONSTR: 80 268.506 21 0.000 22 CONSTR: 29 273.081 22 0.000 23 CONSTR: 50 277.200 23 0.000 24 CONSTR: 9 281.221 24 0.000 25 CONSTR: 4 284.715 25 0.000 26 CONSTR: 41 290.114 26 0.000 27 CONSTR: 19 296.168 27 0.000 28 CONSTR: 10 300.065 28 0.000 29 CONSTR: 17 303.819 29 0.000 30 CONSTR: 26 308.611 30 0.000 31 CONSTR: 77 311.979 31 0.000 32 CONSTR: 84 314.858 32 0.000 33 CONSTR: 2 317.681 33 0.000 34 CONSTR: 7 321.003 34 0.000 35 CONSTR: 23 323.559 35 0.000 36 CONSTR: 24 326.270 36 0.000 37 CONSTR: 62 328.789 37 0.000 38 CONSTR: 33 331.208 38 0.000 39 CONSTR: 82 333.625 39 0.000 40 CONSTR: 61 335.841 40 0.000 41 CONSTR: 22 337.956 41 0.000 42 CONSTR: 72 339.997 42 0.000 43 CONSTR: 31 342.033 43 0.000 44 CONSTR: 64 343.952 44 0.000 45 CONSTR: 34 345.560 45 0.000 46 CONSTR: 60 347.151 46 0.000 47 CONSTR: 71 348.692 47 0.000 48 CONSTR: 13 350.202 48 0.000 49 CONSTR: 28 351.754 49 0.000 50 CONSTR: 87 353.406 50 0.000 51 CONSTR: 90 355.301 51 0.000 52 CONSTR: 15 357.027 52 0.000 53 CONSTR: 37 358.529 53 0.000 54 CONSTR: 40 360.369 54 0.000 55 CONSTR: 57 361.838 55 0.000 56 CONSTR: 81 363.303 56 0.000 57 CONSTR: 78 364.779 57 0.000 58 CONSTR: 69 365.996 58 0.000 59 CONSTR: 68 367.241 59 0.000 60 CONSTR: 35 368.439 60 0.000 61 CONSTR: 47 369.590 61 0.000 62 CONSTR: 85 370.696 62 0.000 63 CONSTR: 88 371.978 63 0.000 64 CONSTR: 1 372.808 64 0.000 65 CONSTR: 30 373.863 65 0.000 66 CONSTR: 73 374.474 66 0.000 67 CONSTR: 46 375.345 67 0.000 68 CONSTR: 45 376.145 68 0.000 69 CONSTR: 53 376.678 69 0.000 70 CONSTR: 74 377.146 70 0.000 71 CONSTR: 70 377.544 71 0.000 72 CONSTR: 56 377.918 72 0.000 73 CONSTR: 76 378.205 73 0.000 74 CONSTR: 25 378.478 74 0.000 75 CONSTR: 20 378.806 75 0.000 76 CONSTR: 11 379.588 76 0.000 77 CONSTR: 12 379.782 77 0.000 78 CONSTR: 38 379.925 78 0.000 79 CONSTR: 58 380.050 79 0.000 80 CONSTR: 8 380.143 80 0.000 81 CONSTR: 51 380.247 81 0.000 82 CONSTR: 36 380.330 82 0.000 83 CONSTR: 44 380.396 83 0.000 84 CONSTR: 55 380.447 84 0.000 85 CONSTR: 48 380.479 85 0.000 86 CONSTR: 39 380.508 86 0.000 87 CONSTR: 59 380.526 87 0.000 88 CONSTR: 14 380.536 88 0.000 89 CONSTR: 63 380.539 89 0.000 90 CONSTR: 67 380.540 90 0.000 Execution begins at 22:50:26.42 Execution ends at

192

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