The McQuaig Mental Agility Test … · The McQuaig Mental Agility Test (MMAT) is a 15 minute (timed) test of general intelligence, also commonly referred to as cognitive ability,

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The McQuaig Mental Agility Test

Technical Manual

Prepared by: Rick D. Hackett, Ph.D.

08/05/2014

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The McQuaig Word Survey® Technical Manual

Contents

©Copyright 1994-2010 The McQuaig Institute® of Executive Development Ltd. Toronto Canada.All rights reserved, including the right to reproduce this report or any part thereof in any manner.

™Registered Trade Mark of the McQuaig Institute of Executive Development

Theoretical Backdrop 5

The Six Basic Personality Profiles 8

Real and Situational Sides of The McQuaig Word Survey 10

Administering, Scoring and Interpreting The McQuaig Word Survey 11

Research on The McQuaig Word Survey 11

Reliability 13

Validity 16

Construct Validity 16

Criterion Related Validity 19

Sales 19

Managers 25

Non Sales / Non Managerial Samples 27

Test Fairness 30

Research on The McQuaig Job Survey (JS) 34

Scale Reliabilities 34

Profile Reliabilities 35

Construct Validity 35

Predictive Validity 36

References 37

Footnotes 41

Bibliographical Notes on Rick D. Hackett 42

Summary Tables 43

Norms 52

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1. Executive Summary

This Technical Manual reports on the psychometric properties of the MMAT as evaluated through scores collected from 6868 respondents. Analyses were performed by education, language, ethnicity, English as first language, industry, sex, and country (i.e. UK, Australia). Results support the MMAT as a reliable assessment of general mental ability. Moreover, validity generalization studies of intelligence tests (i.e. cognitive ability) provide a compelling case for their predictive validity. The analyses reported here show mostly zero to small effects for the categorical variables examined as listed above. The highest group differences in MMAT mean scores were associated with education level and language. Differences in MMAT scores by education level are to be expected. That is, formal education demands cognitive aptitude, with individuals of higher aptitude likely to achieve higher levels of education. There were also modest differences in mean MMAT scores based on whether test takers had English as their first language. However, the stability (reliability) of these differences is not well established as the total number of respondents without English as their first language within the total number of MMAT respondents is relatively small. Overall, the results of these analyses support use of the MMAT to help inform human resources selection decisions.

2. Description of MMAT

The McQuaig Mental Agility Test (MMAT) is a 15 minute (timed) test of general intelligence, also commonly referred to as cognitive ability, or more simply “g”. It is comprised of 50 multiple- choice formatted questions of verbal comprehension, mathematical ability and reasoning. Decades of research have shown that “g” is one of the strongest and most consistent predictors of performance across different performance metrics, jobs, job levels, occupations, organizations, cultures, and demographics (Bertua, Anderson & Salgado, 2005; Ree, & Carretta, 1998; Salgado, Anderson, Moscoso, Bertua, & de Fruyt, 2003; Schmidt & Hunter, 1998). This is not surprising, as “g” reflects the ability to understand, synthesize, and process information -- critical to learning, problem solving and decision making; as well as to communicating information efficiently and effectively (Gottfredson, 1997; Gottfredson, 2002). Accordingly, tests of “g” have become the mainstay of selection systems worldwide.

3. Administration & Scoring

All individuals taking the MMAT are to be given exactly 15 minutes to complete it. Test takers are allowed to use blank sheets of paper to work out solutions, but no calculator. Where the test is administered electronically, safeguards must be in place to ensure that the test is being completed by the person for whom it is intended, that only 15 minutes are allowed, and no calculator or other assistive devices are being used.

Test-takers should be encouraged to try their best to answer as many questions as they can, and be told that: (a) they need not complete the items in order (b); can skip items they find especially difficult or time consuming to answer; (c) they should not expect to complete all 50 items as the test is designed specifically so that very few people are able to do this.

All test takers are to be instructed to work their way through the three sample questions on page 1 of the MMAT before starting the actual test. This is to ensure that the instructions for completing the MMAT are fully understood.

Test takers are also to be assured that their individual test scores will be “safeguarded” (secured, and kept confidential to organizational decision makers alone).

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4. Psychometric Properties:

All individuals taking the MMAT are to be given exactly 15 minutes to complete it. Test takers are allowed to use blank sheets of paper to work out solutions, but no calculator. Where the test is administered electronically, safeguards must be in place to ensure that the test is being completed by the person for whom it is intended, that only 15 minutes are allowed, and no calculator or other assistive devices are being used.

4.1 Reliability

Reliability refers to the degree to which test scores are free of measurement error. With respect to the MMAT, it is important that differences in test scores among people taking the test reflect differences in levels of “g”, and not error associated with a faulty measure. Accordingly, if “g” is considered a fairly stable attribute that can distinguish among individuals, then the same person taking the MMAT over two different administrations, separated by time, should obtain roughly the same score. So, if the total scores of individuals at administration time 1 are correlated with their total scores at administration time 2, the correlation should be quite high (i.e. .70 or over). This is referred to as the test-retest reliability. High test-retest reliability (correlation) coefficients suggest that the MMAT is consistent in its measurement of some individual attribute. This is a desirable characteristic of a test. To illustrate further, imagine if a home food weight scale gave greatly different values for a 1kg of beef each time it is weighed. One would not have much confidence in this scale. Similarly, one would expect a ruler to yield the same metrics each time the length of a single piece of paper is measured. So one of the key requirements of a psychometrically sound assessment of “g” is that it yield consistent measurement, and test-retest reliability is one way to assess this.

Test-retest reliability has been established for the MMAT in a study of 156 university students at Kansas State University conducted by Downey, Wefald and Whitney (2006). The two administrations of the MMAT were separated by 4 weeks, and the test-retest correlation of MMAT scores between the two administrations was .84 -- high by professional standards. A test-retest correlation spans from 0 to 1.0, with higher values reflecting higher reliability. Perhaps not surprisingly, the mean level of performance on the MMAT taken at time 2 was 3.33 points higher than the mean of the MMAT when taken at time 1, suggesting some degree of retention or learning over time. There was no statistical difference in the variance in the distribution of scores at times 1 and 2, suggesting that the MMAT is doing an equally good job of differentiating test takers at both times.

Another means by which to assess the reliability of the MMAT is to split the test in half, perhaps treating all even number items/questions as comprising one half, and all odd numbered items as the other half, then correlating the scores individuals receive on the two halves. High correlations are taken as an index of consistency in responses throughout the test. Psychometricians have devised a way to obtain a robust and stable measure of the internal reliability of a test by calculating the mean correlation between all possible split halves of the test. This mean correlation is referred to as coefficient alpha, and it can range from 0 to 1.0.

Coefficient alpha was computed for all individuals (619 of 6868) who completed all 50 items of the MMAT (9 percent of test-takers), as drawn from the full MMAT databank as described below (i.e. from the databank of all people tested on the MMAT since its inception). It was .83, well above the .70 mark which is considered acceptable by professional standards (Nunnally, 1970).

4.2 Construct Validity

The focal question surrounding construct validity is: “Does the test measure what it was designed to measure”? The MMAT was designed to measure “g” and so should correlate highly and positively with other well established measures of “g”. Downey et al. (2006) reported a .72 correlation with the Wonderlic (formerly known as the

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Wonderlic Personnel Test) -- another well established measure of “g” widely used in HR selection, first developed in 1936. The .72 correlation between the MMAT and the Wonderlic suggests that the MMAT is measuring “g”. While the MMAT is comprised of items which assess verbal fluency, verbal comprehension, mathematical ability and reasoning, sub-scale scores for these three facets of intelligence should not be used for decision purposes, as the MMAT was designed as a measure of overall “g”. Furthermore, many years of research show that “g” scores are normally distributed throughout the general population, and the MMAT, as will be shown later, produces a near normal distribution of scores across populations on which it was administered.

4.3 Predictive Validity

The predictive validity of any individual assessment refers to how well it predicts what it is intended to predict. As applied to HR selection, predictive validity refers to how well the assessment predicts job performance criteria. Predictive validity is assessed by correlating scores on the assessment tool (e.g. a measure of “g”) with a performance metric. Decades of research have shown “g” to be one of the strongest of the available predictors of success in training and job performance for a variety of occupational groups ranging from retail clerks to skilled worker, to managers and executives, with a mean predictive validity coefficient of approximately .50. Catano, Wiesner & Hackett (2012, p. 323-325) provide an overview of the published reviews of the predictive validity of “g” in employee selection.

The most conclusive reviews on the predictive validity of “g” used “meta-analysis”, which essentially calculates the sample size weighted mean of all correlations between “g” and job performance criteria, corrected for various statistical artifacts, across a large number of studies. In addition to showing that “g” is a strong and reliable predictor of performance metrics across jobs, occupational groups, and job levels, the meta-analyses show that the validities hold up across gender, age groups and nations. For the seminal meta-analytic reviews, see Schmidt (2002); Schmidt & Hunter (1998); Salgado et al. (2003); Bertua et al. (2005); and Sackett, Borneman & Connelly (2008). Salgado et al.’s (2003) meta-analysis included data from 10 European Community countries that differed in language, culture, religion and social values and found predictive validities that were even stronger than what had been reported for North American samples. Bertua et al.’s (2005) review included 283 studies conducted in the United Kingdom, and found validities ranging from .50 to .60. Collectively, this research suggests that the predictive validity of cognitive ability transcends language and culture and can be used for HR selection in many countries worldwide (cf. Catano, Wiesner & Hackett, 2013).

5. Legal Defensibility

Political controversy over the use of measures of general intelligence in employee selection has arisen because studies have consistently shown some minority groups (Blacks, Hispanics) to have lower mean scores on these tests than their non-minority counterparts. Where one group consistently underperforms another in a selection contest, this can result in “adverse impact” – where proportionately more non-minorities are hired than minorities. Where a test is shown to have adverse impact, Canadian and U.S. jurisprudence require employers to show (empirically) that the intelligence test is a “bona-fide occupational requirement” (BFOQ) for the job – that intelligence test scores predict performance-relevant metrics, and that there is no alternative, equally predictive test without adverse impact that could be used to inform selection decisions.

The use of general intelligence measures in employee selection has, to date, consistently met this challenge in both Canadian and U.S. courts (Gottfredson, 1986; Cronshaw, 1986; Terpstra, Mohammed & Kethley, 1999). Further, as pointed out in several of these court cases, any adverse impact associated with using intelligence measures to help inform selection decisions does not equate with “test bias”. Bias occurs where predictive validities for a test

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differ across members of minority versus non-minority groups. There is a large and compelling empirical literature showing that intelligence tests are not biased, (Sackett et al., 2008). There are also several studies showing that, when used with candidate information collected from other selection assessments that have no adverse impact (e.g. structured interviews, personality assessments), the adverse impact associated with tests of “g” is largely diminished when overall selection decisions are derived from a weighted combination of all assessment scores (Cortina, Goldstein, Payne, Davison & Gilliland , 2000; Newman, & Lyon, 2009; Schmitt, Rogers, Chan, Sheppard & Jennings 1997).

The importance of using intelligence tests, such as the MMAT, in employee selection, will increase along with the increasing cognitive demands placed on workers encountering fast- paced, changing environments, where they are required to learn and adapt quickly and make decisions of their own in highly unstructured, dynamic and empowering environments.

6. Descriptive Distributional Characteristics for Full MMAT Data Set

All test results on the MMAT were compiled into one large data set, and the descriptive statistics reported below are drawn from this source. The databank as of September 2013 consisted of 6,868 MMAT scores. A detailed breakdown of this population of scores is contained in a supplementary file and available to clients on request. The below distributional characteristics capture categories that allowed interpretable analyses (i.e. cases were collapsed into broader categories as smaller categories did not enable stable results). Percentages as reported below may not add up to exactly 100 due to rounding errors.

6.1 Country

MMAT scores were distributed across 15 different countries, but most were from Australia 3089 (44.98%), UK 1020 (14.85%), Ireland 137 (1.99%), New Zealand 108 (1.57%) and the U.S. 94 (1.37%).

6.2 Sex

Females comprised 2337 (34%) of the cases, males 4528 (66%) --- 3 non reports.

Distribution of Scores on the MMATFemales (N = 2,337)

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Distribution of Scores on the MMATMales (N = 4,528)

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Distribution of Scores on the MMATFemales vs. Males

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Males (N=4528) Females (N=2337)

The above distributions show that MMAT scores clearly approximate a normal distribution, and that they are quite similarly distributed for both males and females.

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6.3 Education

When asked to report their highest level of formal education, respondents gave a myriad of credentials, including formal degrees, certificates, diplomas, and developmental workshops. Some of these categories contained very few observations, so for ease of interpretation, five larger categories were created as follows: bachelor’s degree 1668, (24.29%); graduate degree (i.e. Master’s level or higher), 1147 (16.70%); post secondary certificate or diploma, 989 (14.40%); and high school diploma, 865 (12.59%). Ninety-Six had reported taking formal education that did not fit within any of the above categories (e.g. attended workshops, part- time courses); and 2103 (30.62%) did not provide information on their educational credentials.

6.4 Regional First language

Six categories best captured “first language” as there were dozens of languages reported, with too few people reporting the same language in many cases to allow for stable analyses. The distribution across the six language categories was: English (4257; 61.98%); European (137; 1.99%); Southeast Asian (131; 1.91%); South Asian (99; 1.44%); Middle Eastern (23; .01%); and “Other” (118; 1.72%). Non-respondents totaled 2103 (30.62%).

6.5 Region

MMAT scores distributed by region were as follows: Australasia 3238 (47.15%); UK 1157 (16.85%); US 94 (1.37%); Asia 60 (.87%); and Europe 24 (.35%). There were 2295 (33.42%) respondents who did not complete the “country” question on the MMAT.

6.6 Ethnicity

Ten ethnic categories were created: Australasian 1875 (27.03%); UK 869 (12.65%); South East Asian (189; 2.75%); South Asian 188 (2.74%); European 151 (2.20%); African 119 (1.73%); North American, 112 (1.63%); Middle East 39 (.57%); South American 26 (.38%); and Other 30 (.45%). Non respondents totaled 3270 (47.61%).

6.7 Industry

MMAT scores were distributed across all main categories of industry: Administrative 1171 (17.05%), Sales, 912 (13.28%); Technology 644 (9.34%); Manufacturing, 404 (5.88%); Service 344, (5.01%); Natural Resources, 323 (4.7%); Education, 209 (3.04%); Healthcare 131 (1.91%); and Other 627 (9.13%). Non respondents totaled 2103 (30.62%).

6.8 Job Level

Job levels were collapsed into five groups: “Employee” 2093 (30.47%); Supervisor/Manager 1878 (27.34%); CIO/CEO 345 (5.02%), Student 225 (3.28%); and Other 224 (3.26%). 2103 (30.62%) did not indicate their job level.

6.9 Job Function

MMAT scores were distributed across seven groupings by job function: Management 295 (4.30%), Sales 178 (2.60%), Administrative support 87 (1.23%), Technical 83 (1.21%), Financial 68 (.99%), Operations 51 (.74%), and “Other” 145 (2.11%); 5961 (86.79%) did not indicate their job function.

The frequency distribution of the MMAT scores of the entire dataset approximates a normal distribution, as can be seen below. This is consistent with the well established fact that intelligence scores are normally distributed throughout the population. Given the generally high education of this population of test takers, the scores are more highly concentrated in the upper versus lower end of the distribution, with a mean of 32.19 (S.D. = of 8.67).

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An analysis of covariance (ANCOVA) was run to assess whether MMAT scores differed by the categorical groups listed above. Essentially ANCOVA indicates the amount of variance in MMAT scores explained by any one of the categorical variables statistically controlling for the variance in MMAT scores explained by the other categorical variables. So, for example, when looking at whether MMAT scores differ by regional first language, we need to statistically “control” for level of formal education attained (among other variables), otherwise differences by first language could be due to differences in educational attainment between people whose first language is English versus Non-English. ANCOVA manages this statistical control.

Cases contributing to the ANCOVA totaled 4735 (not the entire 6868), as only cases for which complete data were available on all variables were included in the analysis. Because of the large number of cases included in this analysis, all but two of the categorical variables (age and job function) showed statistical significance at p. < .01, which would suggest that MMAT scores differ by each category with the exception of these two. The large number of cases increases statistical power for detecting such differences, such that while statistically significant, differences may not be substantively (i.e. practically) significant. Specifically, the overall ANCOVA test was statistically significant (p < .01), but the entire set of categorical variables explained only 19% of the variance in MMAT scores. Further, five of these variables explained less than 1% of the variance, and only one (education) explained more than 5% (6.8% -- a “medium effect” – Cohen, 1992); regional first language accounted for 2.8%, and ethnic group 2.7%.

7. Distribution of Mean MMAT Scores for Full Data Set

7.1 Education

Highest mean MMAT scores were obtained by individuals with a university education: Bachelor’s (34.25, s.d. = 8.20), graduate (34.13, s.d. = 8.70); followed by Certificate/non high school diploma (30.27, s.d. = 7.91), and high school (29.31, s.d. = 8.21).

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Distribution of Scores on the MMATEntire Sample (N = 6,868)Frequency Distribution

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Mean MMAT Scores by Education(N = 4,765)

Note: Order of Legend, top to bottom, follows order of bars, left to right.

7.2 Language

Test takers whose mother tongue was English achieved the highest mean MMAT score (33.08, s.d. = 8.30), followed by European (29.35, s.d. = 8.98), South East Asians (26.92, s.d. = 9.00), Middle East (26.91, s.d. = 9.86), South Asians (26.02, s.d. = 8.45) and “Other” (25.95, s.d. = 8.54).

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English (N=4257)

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Middle-Eastern (N=23)

South-East Asian (N=131)

South Asian (N=99)

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Mean MMAT Scores by First Language Group(N = 4,765)


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7.3 Ethnicity

The Mean MMAT scores by ethnic group were as follows: UK (35.45, s.d. = 7.71), North America (34.70, s.d. = 7.74), Australasia (31.98, s.d. = 8.20), European (29.88, s.d. = 8.44), South East Asia (29.13, s.d. = 8.61), South Asia (27.72, s.d. = 8.84), South America (27.62, s.d. = 8.35), Africa (27.07, s.d. = 8.57), and the Middle East (25.00, s.d. = 8.38).

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African (N=119)Australasian (N=1875)European (N=151)Middle Eastern (N=39)North American (N=112)South East Asian (N=189)South American/Caribbean (N=26)South Asian (N=188)United Kingdom/Ireland (N=869)Other (N=30)


Mean MMAT Scores by Ethnic Group(N = 3,598)

7.4 English versus All Non-English Languages Combined

The histogram of MMAT scores obtained from test-takers whose first language was English (N = 4,257) is shown below. The mean MMAT score of these individuals is 33.08 (S.D. = 8.30). In comparison, 508 MMAT test takers for whom English was not their first language obtained a mean MMAT score of 27.17 (s.d. 8.90). Technically differences in mean test scores are not grounds alone to infer that a test is unfairly disadvantageous to one group over the other. The more important issue is whether differences in mean test scores reflect test bias – wherein the scores are not as predictive of performance criteria for one group as compared to the other.

The vast majority of studies comparing “g” scores of white Anglo-Saxons with minority groups (e.g. Hispanics, Blacks) have not found evidence of test bias, and therefore employers have largely refrained from making score adjustments (i.e. such as using standardized scores). Indeed, such score adjustments have been found to be professionally and legally non- defensible (Gottfredson, 1994). Of course, this is a non-issue if MMAT-driven decisions are done within (not across) groups. That is, if MMAT scores are directly compared among all individuals whose mother tongue is other than English, and decisions made with respect to this group of test takers, then differences in mean test scores between groups (English versus Non- English) is irrelevant. Further, with mixed groups, score adjustments for tests of “g” should not be used in the absence of evidence of test bias; and empirical support for such bias in measures of “g” are scant (Sackett et al. 2008).

The distributions of MMAT scores by English and non-English are shown below, followed by a histogram showing mean MMAT scores by regional language.

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Distribution of Scores on the MMATEnglish as First Language (N = 4,257)

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Distribution of Scores on the MMATNon-English First Language (N = 508)

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Distribution of Scores on the MMATEnglish vs. Non-English as First Language

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English (N=4257) Non-English (N=508)

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8. Australia

As the single country contributing the most MMAT scores was Australia (3089, 44.74%), separate analyses were performed on this dataset. The distribution of MMAT scores for Australia is normally distributed (Figure 5), with a mean of 31.73, and standard deviation of 8.38.

As with the all-inclusive dataset, an ANCOVA was conducted to assess whether individuals’ standing on one or more of the categorical variables could explain differences in MMAT scores. Once again, very minor differences in mean scores associated with one’s standing within any one of the categorical variables can be statistically, though not substantively, significant with large datasets due to very high statistical power. Effect sizes were statistically significant (p. < .05) for all but one (age) of the categorical variables. However, the full set of the categorical variables explained only 16 percent of the differences (variance) in MMAT scores.

Not surprisingly, education best predicted differences in MMAT scores, accounting for 8% of the variance. The next most predictive was “first language” (2.1%), followed by industry (1.8%) and ethnicity (1.5%). All the other categorical variables explained less than 1% of the differences (variance) in MMAT scores. As with the all-inclusive data set, these effect sizes are “small” (excepting for education, which is a medium effect; Cohen, 1992), and do not warrant MMAT score adjustments in personnel decisions.

8.1 Education

People with graduate education achieved the highest mean MMAT score (34.14, s.d. = 8.60), followed by bachelors degree (33.89, s.d. = 7.89), Certificate/non-high school diploma (30.06, s.d. = 7.73), and high school diploma (28.88, s.d. 8.10).

Distribution of Scores on the MMATAustralia (N = 3,089)

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Mean MMAT Scores for Australia by Education(N = 3,089)


8.2 Regional Language

People with English as their first language and individuals whose first language was based in the Middle East, had the highest mean MMAT score (32.12, s.d. = 8.21; 32.33, s.d. = 10.78, respectively), followed by Europe (29.98, s.d. = 8.71), South East Asia (26.72, s.d. =8.70) and South Asia (26.22, s.d. = 8.99). A histogram showing MMAT score distributions for Australian test takers by language group is shown below, followed by frequency distributions showing MMAT scores separately by English (32.12, s.d. = 8.21) versus Non-English (combined; mean = 27.42, s.d. = 8.95) as first language. From the frequency distributions it is clear that there is a higher concentration of scores on the right side of the distribution for those whose first language is English; whereas there is a wider spread of scores for their Non-English counterparts. However, the sample size for the Non-English as first language group is relatively small (N=257) compared to the English as first language sample (N = 2,832), so as the former increases it is likely to more closely approximate the MMAT score distribution of the latter; based on the small effect size attributable to language as first language as shown in the ANCOVAs.

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Mean MMAT Scores for Australia by Education(N = 3,089)


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Mean MMAT Scores for Australia by First Language Group(N = 3,089)


Distribution of Scores on the MMATAustralia – English First Language (N = 2,832)

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Distribution of Scores on the MMAT Australia – Non-English First Language (N = 257)

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8.3 Industry

The highest mean MMAT score was obtained by people from the technology industry (33.69, s.d. = 8.38), followed by education (33.41, s.d. = 8.57), administration (32.60, s.d. = 8.11), health care (32.07, s.d. = 9.01), sales, (30.82, s.d. = 7.94), natural resources (30.18, s.d. = 8.32), and manufacturing (30.07, s.d = 8.28).

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Mean MMAT Scores for Australia by Ethnic Group(N = 2,382)

9. United Kingdom

As the single country contributing the second most MMAT scores was the UK 1020 (14.79%), separate analyses were performed on this dataset as well. The distribution of MMAT scores for the U.K. is normally distributed (Figure 8), with a mean of 35.75, and standard deviation of 8.09.

Distribution of Scores on the MMATUnited Kingdom (N = 1,020)

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As with the all-inclusive dataset, an ANCOVA was conducted to assess whether individuals’ standing on one or more of the categorical variables could explain differences in MMAT scores. Once again, very minor differences in scores associated with one’s standing within any one of the categorical variables can be statistically, though not substantively, significant with large datasets because of very high statistical power. Effect sizes were significant (p. < .05) for all but three of the categorical variables (age, job function and job level). However, the full set of the categorical variables explained only 17 percent of the differences (variance) in MMAT scores.

Ethnicity best predicted differences in MMAT scores, accounting for 6.1% of the variance, followed by language (6.0%), education (2.7%), sex (2.0%), and industry (1.8%) – all are small effects with the exception of ethnicity and language, which are medium effect sizes (Cohen, 1992). The effect of ethnicity is likely due to that category consisting of a large predominance of people within the U.K. with English as their first language.

9.1 Ethnicity

North Americans achieved the highest mean MMAT score (42.00, s.d. = 1.73), followed by individuals from Australasia (37.44, s.d. = 6.89), the U.K. (37.18, s.d. = 7.11), Europeans (33.12, s.d. = 6.39), South Asians (32.19, s.d. = 9.05), South East Asians (32.13, s.d. = 12.32), South Americans (29.29, s.d. = 7.48), the Middle East (27.20, s.d. = 10.99), and Africans (26.08, s.d. = 7.79).

05

10152025303540

MM

AT S

core

Ethnic Group



Mean MMAT Scores for UK by Ethnic Group(N = 671)

45

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

The JS is a 21-item instrument that supervisors or other individuals most familiar with a particular job complete. It provides a description of the job in terms of WS profiles. Specifically, once the job profiling is done, individuals with WS profiles most closely matched to the job profile are considered to have the personality disposition most suitable to that particular job. Research suggests that the closer the match between a job profile and the WS profile of an individual, the higher the probability the individual will succeed in the job.

Each of the four dimensions of the WS (Dominance, Sociability, Relaxation and Compliance) is represented within each of the 21 items of the JS. The instrument is completed in the same manner as the WS. The statement which best describes the characteristics of a particular job is indicated by giving an “A” response, the next most characteristic statement is given a “B” response, and so forth.

The supporting research presented below is a summary of a technical report prepared by David Abbey (Evaluation Research Ltd., 1990; Nov.).

Scale reliabilities

Forced-choice tests are, by definition, ipastive. That is, the selection and ranking of alternatives within each item are not independent of one another. Accordingly, calculation of reliability coefficients in the usual manner (split-half or odd- even) may result in over-estimates of individual factor reliabilities. Accordingly, the split- half reliabilities reported for the four dimensions of the JS (N=67) presented below are probably somewhat inflated. They are compared with the split-half reliabilities for the same four dimensions of the WS (N=418).

Research on The McQuaig Job Survey (JS)

Factor JS WS

D .91 .86

S .88 .73

R .78 .80

C .88 .7

Split-half reliabilities for factors of the JS and the WS

Two additional approaches were followed in assessing the internal reliabilities of the four factors of the JS. In the first, split-half reliabilities were calculated for the first choice only within each of the 21 items; in the second, the reliability of the last choice only was calculated. These reflect situations with the maximum and minimum degrees of freedom within items respectively. Additionally, the Spearman-Brown formula was used to correct for the splitting of the 21-item scale. These corrected reliabilities were:

Factor First choice only Last choice only

D .90 .88

S .75 .63

R .88 .71

C .79 .76

These results suggest that the JS has acceptably high degrees of internal consistency across the four factors.

35

Profile reliabilities

Because of the ipsative nature of the JS, a more comprehensive measure of its reliability is recommended. Accordingly, two job profiles were calculated from each of 60 completed JS forms. The first was based on the odd- numbered items, the second based on the even numbered items. Each factor score was pro-rated so that the two profiles yielded a total of 168. The 120 resulting profiles were then translated into the appropriate profile category. A case-by-case comparison of odd and even profiles was then made. Of the 60 cases, 28 were exact matches. That is, when the odd items on the JS yielded a generalist profile, so did the even items; when the odd items yielded a specialist so too did the even items, and so forth. The below contingency Table summarizes the matches and mismatches, after collapsing the individual profiles into Leader, Expert, and Transition. The two sources of profiles are significantly related (Chi- Square = 11.77, d.f. =1, p. < .0001). Because of the small expected values for Transitions, these three cases were omitted from the analysis.

Leader Expert Transition Total

Leader 24 7 0 31

Expert 7 19 1 27

Transition 2 0 0 2

Totals 33 26 1 6

Split-half reliabilities for factors of the JS and the WS

Overall, the above research is supportive of the scale and profile reliabilities of the JS.

Construct validity

One approach to examining the construct validity of the JS would be to assess the correspondence between WS profiles of job incumbents and the JS profiles for their current positions. That is, for persons who are performing existing jobs, we would expect a correspondence between their WS scores and their JS scores, assuming that indi-viduals seek out jobs for which they feel they are most suited.

The correlation between the two scores for each dimension can be regarded as a form of construct validity. As shown below, all four dimensions were significantly correlated between the two instruments.

Factor WS-JS Correlation p. (signif. level)

D .40 .00

S .34 .00

R .22 .04

C .52 .00

It is noteworthy that all four correlations are statistically significant though they are based on two completely different types of assessment. That is, the WS scores result from self-assessment. The JS scores are derived from a supervisor’s assessment of the demand characteristics of a particular job. In completing the JS, supervisors were specifically instructed to assess the job and not the incumbent. Accordingly, the fact that these factors are

36

significantly correlated indicates that there is a significant relationship between the job characteristics and the temperament factors of the incumbents in these jobs.

Additionally, the match between JS and WS profiles on the same sample of job incumbents was assessed. The results are shown below.

Leader Expert Transition Total

Leader 32 9 0 41

Expert 8 15 1 24

Transition 2 0 0 2

Totals 42 24 1 67

WS Profiles

If the two cases for which a Transition profile is indicated by the WS are deleted -- since in reality this would likely result in a re-examination of the expectations of the job -- then the chi-square for independence (Yate’s correction) is 12.32 (p. < .05, d.f. = 2). These results are consistent with the significant correlations between the dimension significant though they are scores of the JS and WS. Together, this research supports the notion that the employees involved in these studies were in jobs which matched their temperaments.

Predictive validity

The true test of the validity of the JS is to determine whether individuals in positions for which JS scores match WS scores are higher performers than those in positions for which JS scores do not match their WS scores. Accordingly, supervisory ratings of performance were assessed against JS-WS matches/mismatches. The two-way contingency table presented below shows the relationship of performance (Lo-Hi) to profile matching.

WS-JS Match Lo Perf. Hi Perf. Total

Matched 19 28 47

Not-matched 13 7 20

Totals 31 35 67

Relationship of Employee Performance to the WS-JS Profile Matching

Of the 47 persons whose profiles were matched on the two instruments, 28 (60%) had high performance levels. Of 20 persons whose profiles were not matched on the two instruments, only 7 (35%) had performance levels above the median. The phi coefficient corresponds to an increase of 22% in the success of predicting the level of performance rating from a knowledge of whether the two profiles were matched (Chi-Square = 3.48, d.f. =1, p. < .10).

Summary of JS validity analysis

This study of 67 job incumbents showed that the job profiles generated by the JS related significantly to the WS profiles of job incumbents. Furthermore, when the JS and the WS profiles matched, it was more likely that the incumbent would receive a higher performance rating than when the profiles did not match.

37

Allport, G.W. & Odbert, H.S. (1933). Trait names: a psycho-lexical study. Psychological Monographs, 47, 171-220 (1, Whole No. 211).

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Eshelman & Townsend (June, 1987a). The McQuaig Institute Zellers managerial study. Technical Report.

Eshelman & Townsend (June, 1987b). McQuaig Institute Prudential study. Technical Report.

Eshelman & Townsend (1987; August). The McQuaig Institute Bretton’s performance of salespeople. Technical Report.

Eshelman & Townsend (1988; Aug.). Performance of Miracle managers: Food-Mart and Ultra-Mart stores. Technical Report.

Eshelman & Townsend (1990; Jan.). Performance of Sainsbury store managers. Technical Report.

Eshelman & Townsend (1991; Sept.). Kmart assistant manager study. Technical Report.

Eshelman & Townsend (1992; Feb.). Kmart operational managers study. Technical Report.

Eshelman & Townsend (1992; March). Friden Alcatel sales applicant study.

Evaluation Research Limited (1987; Nov.). Bretton’s Word Survey data - Statistical analysis II. Technical Report.

Evaluation Research Limited (1990; Nov.). The MJSII (The McQuaig Job Survey®) - Revised: Development, reliability and validity studies. Technical Report.

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Evaluation Research Limited (1991; Oct.). Context study. Technical Report.

References

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Fiske, D.W. (1949). Consistency of the factorial structures of personality ratings from different sources. Journal of Abnormal and Social Psychology, 44, 329-344.

Goldberg, L.R. (1993). The structure of phenotypic personality traits. American Psychologist, 48, No. 1., 26-34.

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Hackett & Associates Human Resources Consultants Inc. (1994; June). The McQuaig Word Survey: Analysis by sex and race with a U.S. student sample. Technical Report.

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Hackett & Associates Human Resources Consultants Inc. (2000, November), Young Entrepreneurs Organization Study. Technical Report.

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Hackett & Associates Human Resources Consultants Inc. (2005a). Australian and Canadian business leaders: A combined and comparative analysis of WS types, profiles and dimension scores.

Hackett & Associates Human Resources Consultants Inc. (2005b). U.S. and U.K. entrepreneurs: A combined and comparative analysis of WS types, profiles and dimension scores.

Hampson, S.E., John, O.P., & Goldberg, L.R. (1986). Category breadth and hierarchical structure in personality: Studies in asymmetries in judgments of trait implications. Journal of Personality and Social Psychology, 51, 37-54.

Hogan, R. (1991). Personality and personality measurement. In M.D. Dunnette & L.M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed., 2, 873- 919). Palo Alto, CA: Consulting Psychologists Press.

Holland, J.L. (1973). Making vocational choices: A theory of careers. Englewood Cliffs, N.J.: Prentice- Hall.

John, O.P. Hampson, S.E., & Goldberg, L.R. (1991). The basic level in personality-trait hierarchies: Studies of trait use and accessibility in different contexts. Journal of Personality and Social Psychology, 60, 348-361.

Levin, J. (1977). Elementary statistics in social research (2nd. Ed.). New York: Harper & Row, Publishers.

Lewis, C. (1991; Dec.). Report on the use of the McQuaig Word Survey and The McQuaig Occupational Test Within Barclays Financial Services, U.K.. Technical Report.

McAllister, L.W. (1986). A practical guide to CPI interpretation. Palo Alto, CA: Consulting Psychological Press.

Mieczyslaw, C. (Undated). To determine the relationship of the Dominance factor to success in sales. Technical Report.

Miller, Ginsburg & Brien (1979; Oct.). Statistical findings. Technical Report.

Miller, Ginsburg & Brien (1980; Jan.). A comparative study of high versus low insurance producers on the McQuaig Word Survey. Technical Report.

Miller, Ginsburg & Brien (1980; Feb.). Test-retest reliability. Technical Report.

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Miller, Ginsburg & Brien (1980; March). Validation study of the Word Survey. Technical Report.

Miller, Ginsburg & Brien (1980a; April). Characteristics of effective versus ineffective Sun Life insurance agents on the McQuaig Word Survey. Technical Report.

Miller, Ginsburg & Brien (1980b; April). Characteristics of effective versus ineffective transit workers on the McQuaig Word Survey. Technical Report.

Miller, Ginsburg & Brien (1980; June). Personality types measured by the McQuaig Word Survey. Technical Report.

Miller, Ginsburg & Brien (1981; April). Correlations of Word Survey scales with other psychological tests. Technical Report.

Nishisato, S. (undated). Predictive validity of McQuaig’s “The Word Survey”. Technical Report.

Parker Allen Company (undated). The Word Survey performance of males and females. Technical Report.

Parker Allen Company (undated). Construct validity of the Word Survey using the 16 Personality Factor Test (16PF). Technical Report.

Parker Allen Company (1981; June). Performance of above average and below average railroad reservation clerks on the McQuaig Word Survey. Technical Report.

Parker Allen Company (1981; Sept.). Predicting performance and turnover of Investor Syndicate agents. Technical Report.

Parker Allen Company (Oct. 1982a). Use of the McQuaig Word Survey to predict new agent Performance. Technical Report.

Parker Allen Company (Oct. 1982b). Use of the McQuaig Word Survey to predict business reporter turnover and performance. Technical Report.

Parker Allen Company (1982; Dec.). Use of the McQuaig Word Survey to predict productivity and longevity of Sun Life insurance agents: A tracking study of new agents. Technical Report.

Parker Allen Company (1983; Jan.). Use of the McQuaig Word Survey to predict performance of Amdahl service field engineers. Technical Report.

Parker Allen Company (1983; May). The Word Survey performance of males and females. Technical Report.

Parker Allen Company (1983; July). Use of the French and English versions of the McQuaig Word Survey. Technical Report.

Parker Allen Company (1983; Aug.). Update on the reliability of factor scores and profile types derived from the McQuaig Word Survey. Technical Report.

Parker Allen Company (1983; Sept.). Use of the McQuaig Word Survey to predict performance of Jamaica agents. Technical Report.

Parker Allen Company (1984; March). The McQuaig Word Survey CEO president study. Technical Report.

Parker Allen Company (1984; May). Use of the McQuaig Word Survey to predict performance of new agents at Montreal Trust: Preliminary report of 12 month data. Technical Report.

Parker Allen Company (1984; Oct.). Use of the McQuaig Word Survey to predict performance of Imperial Life agents:

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Preliminary 6-month analyses. Technical Report.

Parker Allen Company (1985; Feb.). Use of the McQuaig Word Survey to predict performance and tenure of work wear routemen: Preliminary analysis. Technical Report.

Parker Allen Company (1985; March). Use of the McQuaig Word Survey to predict performance of Prudential Assurance agents. Technical Report.

Shizuhiko, N. (Undated). To investigate the predictive validity of the Word Survey in discriminating between successful and unsuccessful managers. Technical Report.

Tett, R.P., Jackson, D.N. & Rothstein, M. (1991). Personality measures as predictors of job performance: A meta-analytic review. Personnel Psychology, 44, 703-742.

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Footnotes

42

Professor of Human Resource Management & Canada Research Chair of Organizational Behaviour and Human Performance Michael G. DeGroote School of Business, McMaster University

Dr. Hackett received his Ph.D in industrial-organizational psychology in 1985 from Bowling Green State University (Ohio) and is a Professor of Human Resources Management at the Michael G. DeGroote School of Business and President of Hackett & Associates Human Resources Consultants Inc.

He is also Past-President of the Canadian Society for Industrial-Organizational Psychology (CSIOP) of the Canadian Psychological Association, a professional association of approximately 170 Canadian industrial-organizational psychologists.

In June, 1997, he was elected “Fellow” of the Canadian Psychological Association.

Additionally, Dr. Hackett is an ad hoc reviewer for several scholarly journals in management and was appointed Editor of the Canadian Journal of Administrative Sciences (Human Resources Division) for a six-year term effective January 1, 1997.

Dr. Hackett was Guest Editor of a special issue of Canadian Psychology, published in 1998, and was elected a member of International Who’s Who of Professionals in October 1997.

He has also co-authored a book titled: “Recruitment and Selection in Canada”, published by ITP Nelson (2005).

Rick D. Hackett

43

Table 1

Study Sample N General Findings

The McQuaig Institute. Reviewed and verified by Hackett & Associates, Human Resources Consultants Inc. (February, 2012)

Global sample, various positions

352,613 Norms• "Real" Side Mean Factor scores: Dominance =

47, Sociability = 47, Relaxation = 33, Compliance = 43

• "Situational" Side Mean Factor scores: Dominance = 44, Sociability = 48, Relaxation = 34, Compliance = 41



352,613 Word Survey Profile Type Distribution• Generalist: 20%; Enthusiast: 16%; Pioneer: 14%;

Specialist: 14%; Persuader 11%;• Transition: 9%; Cooperator: 6%; Possible Transi-

tion*: 6%; Administrator: 4%



352,613 Word Survey Distribution based on Situation AdjustmentsIt was found that 28% of the Generalist Group was holding back while 4% were stretching; 15% of the Specialist Group was holding back while 10% were stretching; 33% of the Transition Group was holding back while 8% were stretching.

Overview of Norms and Profile Distribution

Study Sample N Criterion General Findings

Miller, Ginsburg, & Brien(June 1980)

Managers 418 Internal Consistency • "Real" Side Correlations: Dominance = .87, Sociability = .76, Relaxation = .78, Compliance = .76

• "Situational" Side Correlations: Dominance = .86, Sociability = .73, Relaxation = .80, Compliance = .76

Miller, Ginsburg, & Brien(Feb. 1980)

Unknown 231 Test-Retest (Time Span: Several Months)

• “ Real" Side Correlations: Dominance = .74, Sociability = .57, Relaxation = .72, Compliance = .66


Parker Allen Co. (Aug. 1983)

Various Administrators

121 Test-Retest (Time Span: Several Months)

• "Real" Side Correlations: Dominance = .81, Sociability = .71, Relaxation = .78, Compliance = .82


Parker Allen Co. (Aug. 1983)

Various Administrators

121 Test-Retest (Time Span: Several Months)

• Reliability across the six profile types: Generalist, Pioneer, Specialist, Enthusiast, Cooperator, & administrator is .65 (Cramer's V Statistic)

• When the six profile types are reclassified into "profile families", reliability is .82 (Cramer's V)

Evaluation Research Ltd. ( Jan. 1991)

Various Job Applicants

208 Parallel Forms • Form A (Situational): 72% were accurately matched according to family

• Form A (Real): 73% were accurately matched according to family

• Form B (Situational): 67% were accurately matched according to family

• Form B (Real): 73% were accurately matched according to family

Overview of Reliability Studies

44

Table 2


Eshelman & Townsend (1983)

University Students (M.B.A.)

63 Sixteen Personality Factor Test (16PF)

Significant "Real" Side Correlations for:• WS Dominance - Dominance .45, Rebelliousness .34• WS Sociability – Impulsivity .59, Risk Taking• .58, Sociability .42, Self Sufficiency -.27• WS Relaxation - Risk Taking -.61, Impulsivity -.48,

Sociability -.48, Self• Sufficiency .27, Intelligence .27• WS Compliance - Risk Taking -.52, Impulsivity -.50,

Dominance -.34,• Rebelliousness -.28, Insecurity .32• A very similar pattern of results emerged on the

"Situational" side.

Miller, Ginsburg, & Brien (April, 1981)

University Students (M.B.A.)

78 California Psychological Inventory (CPI)& Rathus Assertiveness Scale (RAS)

Significant "Real" Side Correlations for:• WS Dominance - Dominance .40, Capacity for Status

.40, Sociability .33, Social Presence .37, Psychological Mindedness .30, RAS .52

• WS Sociability - Dominance .37, Sociability .49, Social Presence .40, Self-Acceptance .41, Self-Control -.33, RAS .42

• WS Relaxation - Dominance -.50, Sociability -.45, Social Presence -.40, Self-Acceptance -.43, Self-Control .22, RAS -.56

• WS Compliance - Dominance -.41, Capacity for Status -.34, Sociability -.50, Social Presence -.48, Self-Acceptance -.34, Responsibility .30, Femininity .27, RAS -.56

• A very similar pattern of results emerged on the "Situational" side.

Hackett & Associates, Human Resources Consultants Inc. ( July 1996)

University Students (3rd & 4th year Commerce)

206 NEO Five Factor Inventory (NEO-FFI)

Significant "Real" Side Correlations with NEO-FFI:• WS Dominance - Agreeableness -.40• WS Sociability - Extraversion .58• WS Relaxation - Extraversion -.43• WS Compliance - Agreeableness .34

WS Profile Types can be differentiated based on their overall patterns of scores on the NEO-FFI:

• WS Leaders are less "neurotic" and less "agreeable" than WS Experts.

• Cooperators are more "neurotic" than Persuaders; Specialists are more "introverted" than all other profile types except Pioneers; Cooperators are more "agreeable" than Generalists and Pioneers.

Hackett & Associates, Human Resources Consultants Inc. ( July 1996)

University Students (3rd & 4th year Commerce)

206 Myers-Briggs Type Indicator (MBTI)

• Most WS Leaders are classified as ISTJs (serious, quiet, thorough, logical, realistic, dependable, persevering, and independent minded).

• Most WS Experts are classifies as ISFJs (quiet, friendly, responsible, conscientious, stable, thorough, accurate, non-technical, patient with details, loyal, and considerate of others).

Miller, Ginsburg, & Brien (March, 1980)

Employees of 14 Different Corporations

200 ObservableBehaviours (Supervisory Checklist)

• WS profile patterns "map onto" or reflect behaviours across six areas (verbal communication, ability to work under pressure, level of autonomy, interpersonal skills, work habits, achievement orientation).

Overview of Construct Validity Studies

45

Table 3


Miller, Ginsburg, & Brien(April 1980)

Life Insurance Agents

184 Supervisory Performance Ratings

• Profile type is significantly related to agents' performance levels (Validity Coefficient = .44)

• Generalists/Pioneers are the best performers; Transition/Holding Back types are the poorest performers

• Generalists/Pioneers are the least likely to be terminated whereas Transition/Holding Back types are the most likely to be terminated

• Better performers tend to have slightly higher Sociability scores

Miller, Ginsburg, & Brien( Jan. 1980)


97 Annual Sales • Top performers have higher Dominance scores and lower Sociability and Compliance scores on the "Situational" side of the WS than low performers

• Top performers have higher Dominance scores on the "Real" side than low performers

Parker Allen Co. (Dec. 1982) Life Insurance Agents

184 Turnover After3 Years

• Transition/Holding Back types are most likely to turnover (89%)

• Generalists/Pioneers are least likely to turnover (38%)• Other WS types are in between (50%)

Parker Allen Co. (Oct. 1984) Life Insurance Agents

62 Sales Performance (Commissions)

• Generalists/Pioneers are more likely to earn high commissions than other profile types

Choynowski (Undated) Various Salesmen 187 Managerial Performance Ratings

• Higher performers obtain higher Dominance scores and lower Relaxation and Compliance scores on the "Real" side of the WS

Parker Allen Co. (Sept. 1981) Investment Firm Agents

249 Supervisory Performance Ratings and Turnover

• Generalists/Pioneers are the best performers; Transition/Holding Back types are the poorest performers

• Transition/Holding Back types are more likely to be dismissed than Generalist/Pioneers

• WS type contributes significant incremental variance to the prediction of performance (over the prediction provided by 8 other predictors)

Parker Allen Co. (Oct. 1982) Real Estate Agents 139 Sales Performance • Generalists/Pioneers outperform Enthusiasts/Specialists and Transition/Low Morale types and are least likely to turnover

• Transition/Holding Back types are the poorest performers

Parker Allen Co. (May 1984) Real Estate Agents 139 Sales Performance and Turnover

• Generalists/Pioneers outperform Enthusiasts/Specialists and Transition/Low Morale types Transition/Holding Back types are the poorest performers and have the highest turnover rate

Eshelman & Townsend (July 1986)

Retail Sales Representatives

150 Performance Ratings

• The poorest performers are the Specialists/Cooperators

• The poorest performers have higher than average Compliance scores on the "Situational" side of the WS

• Good performers have higher Dominance scores and lower Compliance scores on the "Real" side of the WS than average or poor performers

Eshelman & Townsend (Aug. 1987)



• Generalists and Enthusiasts are the best performers• Cooperators are the poorest performers

Overview of Criterion Validity Studies with Sales Occupations

46

Table 3 (Continued)

Overview of Construct Validity Studies


Evaluation Research Ltd. (Nov. 1987)


321 Turnover • Leaders (Generalists, Persuaders & Pioneers) have higher turnover rates than Experts (Specialists, Cooperators & Enthusiasts

• Experts who have low Relaxation scores tend to be the best performers

Curtis Industries (Oct. 1986) Sales Representatives

55 Sales Performance • Enthusiasts are the best performers, followed by Generalists and then Specialists

• Holding Back types are the poorest performers• Poor performers have higher Dominance scores

and lower Compliance scores than average and high performers

Parker Allen Co. (Sept. 1983) Life Insurance Agents

62 Sales Performance • Generalists/Pioneers are the highest performers• Transition/Holding Back types are the lowest

performers• Specialists/Cooperators/Enthusiasts fall between

the other types in their performance

Parker Allen Co. (March 1985)



• The correspondence between predicted and actual performance is indicated by a 2/3rds "hit rate"

Eshelman & Townsend (June 1987)



• Cooperators, Generalists & Specialists are the best performers

• Enthusiasts are the poorest performers

Lewis (Dec. 1991) Financial Services Sales

244 Sales Performance • The factor scores on the WS permit the identification of good vs. poor performers

• There is an overall performance benefit of approximately 11% to be gained from using the WS as a selection instrument

Eshelman & Townsend (March 1987)

Kraft Territory Managers (Sales)


• Generalists and Pioneers outperform all other profile types

• High performers have higher Dominance scores and lower Relaxation and Compliance scores than lower performers

Hackett & Associates Human Resources Consultants Inc. (August 1995)

RBC Security Agents 134 Total Assets • WS "Real C" correlated with total assets (r=.30, p < .05).

• Those with "Real C" scores of 18 or less realized mean assets of $3,720,331;

• Those with "Real C" scores between 19-30 realized mean assets of $5,561,063;

• Those with "Real C" scores of 31 or more realized mean assets of $7,637,156.50

47

Table 4


Nishisato (Undated) Various Managers 102 Performance Ratings

• Managers' performance levels were predicted 92.9% of the time based on their WS factor scores

• Successful managers obtain higher scores on Dominance and Sociability and lower scores on Relaxation and Compliance than unsuccessful managers

Eshelman & Townsend (Aug. 1988)

Grocery Chain Managers


• Pioneers are the best performers• Successful managers have higher than average

Dominance scores and lower than average Relaxation and Compliance scores on the "Situational" side of the WS

• On the "Real" side, strong performance is associated with higher than average Dominance Scores and lower than average Compliance scores

Eshelman & Townsend (Sept. 1991)

Kmart Assistant Store Managers


• Generalists, followed by Pioneers, are the highest performers

• High performance is associated with higher Dominance scores, satisfactory performance is associated with higher Sociability scores, and poor performance is associated with higher Relaxation and Compliance scores

Eshelman & Townsend (Feb. 1992)

Kmart Operational Managers


• Pioneers and Generalists are the highest performers• Better performers have higher Dominance scores• Higher Relaxation and Compliance scores are

associated with poorer performance

Eshelman & Townsend (June 1987)

Zellers Store Managers


• Pioneers are the highest performers• The best performers achieve higher Dominance and

lower Relaxation scores than the average or lowest performers

Parker Allen Co. (March 1984)

Corporate Presidents & CEOs

75 Attainment of Presidency/CEO Status

• Over 90% of CEOs/Presidents are Pioneers (41.3%), Generalists (37.3%), or Administrators (16%)

Eshelman & Townsend (Jan. 1990)

Sainsbury Store Managers


• Pioneers and Administrators are most likely to be top performers

• Specialists and Holding Back types are most likely to be the poorest performers

• Top performers achieve higher Dominance scores than poor performers

• Poor performers have higher Sociability and Relaxation scores than top performers on the "Situational" side of the WS and higher Compliance scores on the "Real" side

Overview of Criterion Validity Studies with Managers

48

Table 5

Overview of Criterion Validity Studies with Non Sales/Non Managerial Samples


Miller, Ginsburg & Brien(April, 1980)

Transit Workers 25 Performance Ratings

• High Relaxation scores and low Sociability scores are associated with higher performance

Parker Allen Co. (Oct. 1982) Business Reporters 180 Performance & Turnover

• Generalists and Pioneers are least likely to turnover• Holding Backs and Specialists/Enthusiasts/

Cooperators are most likely to turnover• WS profile is unrelated to job performance

Parker Allen Co. ( Jan. 1983) Field &Service Engineers


• Among Service Engineers the highest performers are Cooperators, Specialists and Administrators

• The best performing Service Engineers have higher Relaxation scores and lower Dominance scores than poorer performers

• Among Field Engineers, the highest performers are Administrators, Specialists, and Transitions

• The best performing Field Engineers have higher Relaxation scores and lower Dominance and Sociability scores than poor performers

• For Field and Service Engineers combined, Dominance correlates negatively and Relaxation correlates positively with performance

Parker Allen Co. (Feb. 1985) Work Wear Routemen

89 Performance Ratings and Turnover

• Generalists, Pioneers, Administrators and Persuaders are the best performers

• Transition/Holding Back types have the highest involuntary dismissal rates whereas Enthusiasts/Specialists/Cooperators have the highest voluntary resignation rates

Parker Allen Co. ( June 1981) Railroad Reservation Clerks

86 Past & Present Supervisory Performance Ratings

• Present performance ratings are unrelated to WS profile type

• Generalists receive the highest past performance ratings, then Specialist, then Enthusiasts and, finally, Transitions receive the lowest past ratings

• Relaxation is related positively with performance whereas Sociability is related negatively

Hackett & Associates (2009) Claims 105 Performance & Potential ratings Ratings

• Among claims representatives the Specialist and the Enthusiast received the highest performance ratings

• The Good category has a higher percentage of Expert profiles (59%) than Leader profiles (33%)

• The Superior category also has a higher percentage of Expert profiles (54%) than Leader profiles (32%)

• Lateral Potentials have a greater percentage of Expert profiles (55%) than Leader profiles (38%)

• Promotion Potentials have a greater percentage of Expert profiles (65%) than Leader profiles (26%). ꭕ2 (1, 23) = 3. 52, p < 06.

Hackett & Associates (2009) Underwriting 89 Performance & Potential ratings Ratings

• The Specialist is the most common profile in the Good (35%) and Superior (30%) groups, ꭕ2 (7, 129) = 16.5, p < .05

• The Superior category has a higher percentage of Leader profiles (47%) than Expert profiles (40%) while the Good category has a higher percentage of Expert profiles (68%) than Leader profiles (24%), ꭕ2 (1, 116) = 8.8, p < .01

• Lateral Potentials have a greater percentage of Expert profiles (71%) than Leader profiles (24%), while more individuals with Promotion Potential have Leader profiles (46%) than Expert profiles (36%), ꭕ2 (1, 118) = 10.5, p < .001

49

Table 6


Hackett & Associates HRC. Inc. (Nov. 2000)

Young Entrepreneurs Organization

1279 males239 females

Revenues of $1,000,000 or more

• 79% were WS Leaders; 80.2% of the males and 70.7% of the females.

• 60% of sample was comprised of Generalists (31.1%) and Pioneers (28.9%)

• Most common among females were Generalists (32.2%) and Persuaders (20.9%)

• WS dimension profile for total sample one of high Dominance, next highest Sociability, with substantially lower levels of Relaxation and Compliance

• Females were similarly high in Dominance and Sociability; Males were higher on Dominance than on Sociability

Overview of Criterion Validity Studies with Entrepreneurs

50

Table 7

Overview of Test Fairness Studies


Parker Allen Co. (May 1983)

Male & Female MBA Students

40 28

Mean WS Factor Scores by Sex

• There are no differences between males and females except that females have slightly higher Sociability scores on the "Real" side of the WS

• Males and females are equally distributed across profile types

Parker Allen Co. ( July 1983)

Male & Female Students

73 131

Mean WS Factor Scores by Sex

• There are no differences between males and females on distribution across WS profile types nor on mean factor scores

Parker Allen Co. (Oct. 1982a)

Male & Female Sales Agents

64 75

WS Profile Type by Sex and Age

• There are no differences in WS profile types associated with either sex or age

Parker Allen Co. (Oct. 1982b)

Male & Female Business Reporters

126 54

WS Profile Type by Sex

• There are no differences in WS profile types associated with sex


Male & Female Managers

178 39

WS Profile Type by Sex, Education, Age and Race

• There are no differences in WS profile types associated with sex, education, age, or race

Eshelman & Townsend (Sept. 1991)

Kmart Assistant Managers (Mixed Sample)

188 WS Profile Type by Sex, Race, Age, Language, Education and Marital Status

• There are no differences in WS profile types associated with sex, race, age, language, education, or marital status

Lewis (1991) Financial Services Sales

244 WS Profile Type by Sex

• There are no differences in WS profile types associated with sex


Applicants for Sales Positions

1,813 WS Profile Typeby Sex and Race

• There are no differences in WS profile types associated with sex or race

Parker Allen Co. ( July 1983)

McGill University Students

204 WS Profile Typeand Factor Scoresby Language Version of WS English (n = 105) French (n = 99)

• There are no differences in WS profile types associated with the language version of the WS administered (English vs. French)

• Francophones are more compliant and sociable and less relaxed than Anglophones

Hackett & Associates HRC Inc. ( June, 1994)

U.S. University Students

267 WS Profile Typeand Factor Scoresby Sex and Race

• There are no differences in WS profile types associated with sex or race.

• Higher Dominance Scores (Real & Situational) for males• Higher Compliance Scores for females (Situational)• Higher Sociability Scores (Real) for Whites over Mexican

Americans• Higher Dominance Scores (Real) for African Americans

over Whites

Hackett &Associates HRC Inc. (May, 1996).

U.S. University Students (Business)

516 WS Profile Types and factor scores by language version of WS, Sex, and Race English (n=292) Spanish (n=224)

• There are fewer Generalists, fewer Leaders and more Transitions among Hispanics writing the Spanish version than among the whites, African Americans or Mexican Americans writing the English version.

• On WS-Situational, Hispanics writing the Spanish version have lower Dominance scores than African Americans, lower Sociability scores than Whites, and higher Compliance scores than African Americans or Whites. Whites have higher Sociability scores than Mexican-Americans.

• On WS-Real, African Americans have higher dominance scores than Whites or Hispanics, lower Relaxation scores than Hispanics, and lower Compliance scores than Hispanics

• There are no differences in WS profile types nor in WS Factors associated with sex on either language version.

51

Table 7 (Continued)


Young Entrepreneurs Organization (YEO) – Hackett & Associates HRC. Inc. (Nov., 2000).

Male & Female Entrepreneurs (38 years of age or younger)

1279 males239 females

WS Profile Types and Factor Scores by Sex

• Approximately 79% of YEO members were typed as WS-Leaders; 80.2% Males, 70.7% females. More females (18.4%) than males (11.1%) were typed as Experts.

• The most common WS profile type for males was Pioneer (31.2%) and Generalist (30.8%) whereas for females it was Generalist (32.2%) and Persuader (20.9%).

• Higher Dominance scores (Real and Situational) for males

• Higher Sociability scores (Real and Situational) for females

Hackett & Associates HRC Inc. (2001; April)

350 university business students; Canada, China & India

105 Canada 136 China 109 India

WS profiles and WS types by Country and by Gender

• No differences in WS responses between Indian males and Indian females

• No differences in responses between Chinese males and Chinese females

• No differences in responses between males and females across three countries

• No cross-country differences in WS profiles or WS types

Hackett & Associates HRC Inc. (2005; September)

1654 Entrepreneurs

1509 U.S. (YEO)145 U.K. (Business Link)

WS Profile Types and Factor Scores by Gender and by Nation

• Approximately 79% of this group was typed as Leaders• No nation difference in WS types and profiles• Males scored higher on Dominance (both Situational

and Real) and Relaxation (both Situational and Real)• Females scored higher on Sociability (both Situational

and Real) and on Real Compliance• Males from the U.K. scored higher on Situational

Sociability than did males from the U.S.

Hackett & Associates HRC Inc. (2005; September)

763 business leaders

152 Canada611 Australia635 senior executives128 managers679 males84 females

WS Profile Types and Factor Scores by Gender, by Nation and by senior executives/ managers

• Most typed as WS Leaders, followed by Transitions or Experts for the total sample and in each separate sample

• No gender and nation difference in distribution of WS types and profiles in the total sample and in each separate sample

• Greater representation of Leaders among senior executives (77%) than among managers (60%)

• Greater percentages of Experts and Transitions among managers than among senior executives (19% versus 13% and 21% versus 10%, respectively).

• Highest in Dominance, next highest in Sociability, with substantially lower levels of Relaxation and Compliance for the total sample and in each separate sample

• Canadian (and male Canadian) senior executives scored higher than Australian (and male Australian) senior executives on Compliance (Situational)

• Female senior executives scored higher than males on Sociability (Situational and Real) and lower on Relaxation (Situational)

• Canadian managers scored lower than Australian managers on Relaxation (Situational and Real)

• Both male managers and female managers from Canada scored significantly lower on Relaxation (Situational) than did their Australian counterparts.

• For senior executives and managers together, Canadians scored significantly higher on Compliance (Situational) and lower on Relaxation (Real)

• Females scored significantly higher on Sociability (both Situational and Real) and lower on Relaxation (both Situational and Real)

• Senior executives scored higher than managers on Dominance (both Situational and Real) and lower on Compliance (both Situational and Real)

Overview of Test Fairness Studies

52

Distribution of WS Dimensions and Profile Types Total Sample* (N=352,613)

Dimension Scores

0

10

20

30

40

50

44

48

34

41

47 47

33

43

Situational (N=) Real (N=)

Mea

n Sc

ores

Dominance RelaxationSociability Compliance

Generalist20%

Administrator4%

Pioneer14%

Possible Transition6%

Transition9%

Cooperator6%

Enthusiast16%

Specialist14%

Persuader11%

Distribution of WS Dimensions and Profile Types Total Sample* Males (N=197,941)

Dimension Scores

0

10

20

30

40

50

47 46

34

41

50

45

34

40


Mea

n Sc

ores


Generalist23%

Administrator6%

Pioneer18%


Transition9%

Cooperator5%

Enthusiast12%

Specialist13%

Persuader10%

*These norms represent data collected predominantly from the corporate business population and not necessarily the entire workplace population.

53

Distribution of WS Dimensions and Profile Types Total Sample* Females (N=154,697)

Dimension Scores

0

10

20

30

40

50

60

41

51

34

43 43

50

33

42


Mea

n Sc

ores


Generalist17%

Administrator4%

Pioneer10%


Transition9%

Cooperator8%

Enthusiast21%

Specialist15%

Persuader12%

Distribution of Word Surveys based on Situational Adjustments (N=352,613)

Holding Back24%

Not Stretchingor Holding Back

69%

Stretching7%


54

Distribution of Word Surveys based on Situational AdjustmentsGeneralist Group Only (N=175,497)

Holding Back28%


68%

Stretching4%

Distribution of Word Surveys based on Situational AdjustmentsSpecialist Group Only (N=128,062)

Holding Back15%


75%

Stretching10%


55

Distribution of Word Surveys based on Situational AdjustmentsTransition/Possible Transition and Balanced Groups Only (N=49,054)

Holding Back33%


59%

Stretching8%

Distribution of WS Profile Types Chief Executive Officers (N=710)

Specialist8%

Administrator13%

Persuader7%

Enthusiast2%

Transition9%

Cooperator2%

Pioneer29%

Generalist30%


56

Distribution of WS Dimensions and Profile Types Entrepreneurs (N=1654)

Dimension Scores

0

10

20

30

40

50

60

70

80

63.3

49.2

27.6 27.9

62.7

47.5

2831.1


Mea

n Sc

ores


Generalist30%

Administrator6%

Pioneer28%


Transition6%

Cooperator2%

Enthusiast4%

Specialist6%

Persuader14%

Distribution of WS Dimensions and Profile Types Managers (N=703)

Dimension Scores

0

10

20

30

40

50

60

52.8

42.1

35.7 37.2

56.1

40.6

34.337.2


Mea

n Sc

ores


Generalist18.1%

Administrator8.9%

Pioneer22.9%

Transition11.2%

Cooperator2.9%

Enthusiast8.7%

Specialist15.5%

Persuader6.8%

Holding Back4.9%


57

Distribution of WS Dimensions and Profile Types Sales* (N=2,760)

Dimension Scores

0

10

20

30

40

50

60

44.2

52.5

31.8

39

48.250.6

30.7

37.8


Mea

n Sc

ores


Generalist36.5%

Administrator1.5%

Pioneer9%

Holding Back8.5%

Transition4%

Cooperator2.5%

Enthusiast10.5%

Specialist2.5%

Persuader25%

Distribution of WS Dimensions and Profile Types Life Insurance Agents (N=234)

Dimension Scores

0

10

20

30

40

50

60

4851.3

34.2 35.2

51.448.3

32.435.8


Mea

n Sc

ores


Generalist 4%Administrator 2%

Pioneer 3%

Transition3%

Cooperator9%

Enthusiast10%

Specialist15%

Persuader4%

Holding Back50%


58

Distribution of WS Dimensions and Profile Types Service Occupations* (N=217)

Dimension Scores

0

10

20

30

40

50

44.3

48.9

35

39.2

43.9 42.4

33.6

39.8


Mea

n Sc

ores


Administrator30.3%

Pioneer13.1%

Holding Back14.1%

Transition7.1%

Cooperator3%

Enthusiast15.2%

Specialist17.2%

Distribution of Profile Types Administration (N=357)

Specialist25%

Administrator6%

Persuader4%

Enthusiast13%

Transition9%

Cooperator7%

Pioneer16%

Generalist21%


59

Distribution of WS Dimensions and Profile Types Skilled Workers* (N=93)

Dimension Scores

0

10

20

30

40

50

39.1

43.441.4

39.5 38.441.3 42.1

45.9


Mea

n Sc

ores


Administrator10%

Holding Back22%

Transition10%

Cooperator16%

Enthusiast16%

Specialist26%

Distribution of WS Dimensions and Profile Types University Students (N=1478)

Dimension Scores

0

10

20

30

40

50

41.4

48.73

38.89 38.87

43.4147.09

37.5539.84


Mea

n Sc

ores


Generalist19%

Administrator4%

Pioneer10%

Transition13%

Cooperator8%

Enthusiast17%

Specialist19%

Persuader9%

Holding Back1%


60

Distribution of WS Dimensions and Profile Types MBA Students (N=283)

Dimension Scores

0

10

20

30

40

50

37

48

4240 41

47

3942


Mea

n Sc

ores


Generalist16%

Administrator4%

Pioneer6%

Transition13%

Cooperator11%

Enthusiast15%

Specialist16%

Persuader10%

Holding Back6%

Distribution of Profile Types by Age

Specialist15%

Administrator4%

Persuader9%

Enthusiast14%

Transition13%

Cooperator8%

Pioneer15%

Generalist20%

Holding Back1%

Specialist14%

Administrator4%

Persuader9%

Enthusiast9%

Transition20%

Cooperator5%

Pioneer13%

Generalist22%

Holding Back2%


Less than 30 (N=633) 30 to 40 (N=198)

61

Distribution of WS Dimensions and Profile Types English Version (N=7704)

Dimension Scores

0

10

20

30

40

50

60

51.3648.1

33.89 34.65

52.77

45.54

33.1136.1


Mea

n Sc

ores


Generalist29%

Administrator4%

Pioneer17%

Transition7%

Cooperator4%

Enthusiast9%

Specialist9%

Persuader15%

Holding Back6%

Distribution of WS Dimensions and Profile Types French Version (N=119)

Dimension Scores

0

10

20

30

40

50

60

49

43

33

43

52

40

31

45


Mea

n Sc

ores


Generalist23.2%

Administrator25.3%

Pioneer6.1%

Transition14.1%

Cooperator1%

Enthusiast5.1%

Specialist23.2%

Persuader2%


62

Distribution of WS Dimensions and Profile Types Spanish Version (N=211)

Dimension Scores

0

10

20

30

40

50

38

47.8

37.2

44.9

37.3

48.4

36.9

45.3


Mea

n Sc

ores


Generalist 6.2%Administrator 3.8%

Pioneer 4.7%

Transition22.8%

Cooperator10.4%

Enthusiast22.8%

Specialist19.4%

Persuader9.9%

Distribution of WS Dimensions and Profile Types African American (N=269)

Dimension Scores

0

10

20

30

40

50

60

44.7

52.1

34.636.8

48 48.9

32.7

39.2


Mea

n Sc

ores


Generalist 33.1%

Administrator 2.6%

Pioneer 12.3%

Transition10%

Cooperator4.8%

Enthusiast14.5%

Specialist5.6%

Persuader17.1%


63

Distribution of WS Dimensions and Profile Types Asian American (N=211)

Dimension Scores

0

10

20

30

40

50

37.7

48.9

34.3

47

35.7

46.8

35.7

49.2


Mea

n Sc

ores


Generalist 20.8%

Administrator 0%

Pioneer12.5%

Cooperator8.3%

Enthusiast20.8%

Specialist16.7%

Persuader4.2%

Transition16.7%

Distribution of WS Dimensions and Profile Types Mexican-American (N=334)

Dimension Scores

0

10

20

30

40

50

39.6

47

37.6

43.8

39.9

47.3

37.7

43


Mea

n Sc

ores


Generalist 10.5%

Administrator 3.6%

Pioneer8.4%

Cooperator9.9%

Enthusiast22.7%

Specialist19.5%

Persuader9%

Transition16.4%


64

Distribution of WS Dimensions and Profile Types India (N=109)

Dimension Scores

0

10

20

30

40

50

36

49

42

36

41

48

39 38


Mea

n Sc

ores


Generalist18%

Administrator4%

Pioneer8%

Cooperator11%

Enthusiast13%

Specialist13%

Persuader15%

Transition8%

Holding Back7%

Distribution of WS Dimensions and Profile Types Romania (N=131)

Dimension Scores

0

10

20

30

40

50

37

50

39 3840

48

38.5 37


Mea

n Sc

ores


Generalist11%

Administrator4%

Pioneer9%

Cooperator8%

Enthusiast14%

Specialist16%

Persuader16%

Transition9%

Holding Back6%


65

Distribution of WS Dimensions and Profile Types North America (N=7659)

Dimension Scores

0

10

20

30

40

50

60

52.0748.59

34.03 35.35

54.61

47.27

34.4337.85


Mea

n Sc

ores


Generalist27%

Administrator4%

Pioneer17%

Cooperator4%

Enthusiast10%

Specialist9%

Persuader14%

Transition8%

Holding Back6%

Distribution of WS Dimensions and Profile Types China (N=136)

Dimension Scores

0

10

20

30

40

50

34

45

4042

38

44

3943


Mea

n Sc

ores


Generalist12%

Administrator 4%

Pioneer 3%

Cooperator13%

Enthusiast17%

Specialist17%

Persuader7%

Transition19%

Holding Back4%


66

Distribution of WS Dimensions and Profile Types White (N=2,106)

Dimension Scores

0

10

20

30

40

50

60

39.3

52.8

37.3 38.1 39.8

52.4

35.540.2


Mea

n Sc

ores


Generalist36.9%

Administrator3%

Pioneer9.7%

Cooperator3.3%

Enthusiast8.8%

Specialist4.8%

Persuader26.5%

Transition7%

Distribution of WS Profile Types Unidentified Non-White (N=91)

Specialist8%

Administrator13%

Persuader7%

Enthusiast2%

Transition9%

Pioneer29%

Generalist30%


67

Distribution of WS Profile Types by Age

Specialist15%

Administrator4%

Persuader9%

Enthusiast14%

Transition13%

Cooperator8%

Pioneer15%

Generalist20%

Holding Back1%

Specialist14%

Administrator4%

Persuader9%

Enthusiast9%

Transition20%

Cooperator5%

Pioneer13%

Generalist22%

Holding Back2%

Distribution of WS Profile Types by Age Over 40 (N=75)

Specialist 1%

Administrator3%

Persuader13%

Enthusiast 3%

Transition24%

Cooperator8%

Pioneer21%

Generalist27%


Less than 30 (N=633) 30 to 40 (N=198)

68

Distribution of WS Profile Types by Education

Specialist15%

Administrator4%

Persuader9%

Enthusiast14%

Transition13%

Cooperator8%

Pioneer15%

Generalist20%

Specialist18% Administrator

7%

Persuader11%

Enthusiast17%

Transition9%

Cooperator 8%

Pioneer13%

Generalist17%

Specialist8%

Administrator4%

Persuader8%

Enthusiast13%

Transition17%

Cooperator5%

Pioneer24%

Generalist21%

Specialist14%

Administrator4%

Persuader14%

Enthusiast11%

Transition20%

Cooperator7%

Pioneer11%

Generalist19%


College (N=60) University (N=88)

High School (N=155) Some Post-Secondary (N=300)

69

Distribution of WS Profile Types by Marital Status

Specialist12%

Administrator6%

Persuader11%

Enthusiast16%

Transition 12%

Cooperator5%

Pioneer19%

Generalist19% Specialist

15.2%

Administrator6.1%

Persuader4%

Enthusiast13.1%

Transition12.1%

Cooperator5.1% Pioneer

26.3%

Generalist18.2%


Single (N=160) Married (N=93)

Documents

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