A First Study of the Multidimensional Students’ Life Satisfaction Scale with Adolescents

RICH GILMAN, E. SCOTT HUEBNER and JAMES E. LAUGHLIN

A FIRST STUDY OF THE MULTIDIMENSIONAL STUDENTS’LIFE SATISFACTION SCALE WITH ADOLESCENTS

(Accepted 12 November, 1999)

ABSTRACT. The psychometric properties of the Multidimensional Students’Life Satisfaction Scale (MSLSS: Huebner, 1994) were assessed with a sample ofadolescents from grades 9–12. Results of confirmatory factor analyses supportedthe hypothesized factor structure. Further, evidence for the convergent anddiscriminant validity of the instrument was obtained. Acceptable internal consist-ency estimates for the Total and domain scores were also obtained. Thesepsychometric properties were similar to findings of the MSLSS with youngerchildren. Limitations and future research considerations were discussed.

The experience of happiness has been considered to be the supremegoal of human existence (Annas, 1993; Kamman et al., 1984).Conversely, to admit unhappiness is often considered to be the ulti-mate concession of failure that an individual can make (Sandvik,Diener and Seidlitz, 1993). This negative experience is regarded as aprincipal correlate of psychological distress (Basche, 1995; Lewin-sohn et al., 1991) and interpersonal problems (Furr and Funder,1998).

Given the perceived importance of subjective well-being (SWB),researchers investigating this area are concerned with how andwhy people experience their lives in positive ways (DeNeve andCooper, 1998; Diener, 1984; Diener et al., 1999). The findingsof over three decades of SWB research (see Diener et al., 1999)have provided an important and useful complement to psychology’straditional focus on unhappiness and ill-being (Pavot and Diener,1993), and have stimulated calls to formulate effective interven-tion strategies designed to enhance SWB among individuals whoremain unhappy (Cowen, 1991, 1994; Diener and Diener, 1996;Furr and Funder, 1998; McGuire and Earls, 1991; Seligman, 1998).Such enhancement is expected to subsequently serve as a protective

Social Indicators Research52: 135–160, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

136 RICH GILMAN ET AL.

barrier against potential factors leading to various forms of psycho-pathology (Cowen, 1994; Durlak and Wells, 1997; Frisch, Cornell,Villenueva and Retzlaff, 1992).

The most commonly accepted model of subjective well-beingconceptualizes the construct as comprising an affective component(i.e., positive and negative emotions) and a cognitive componentlabeled life satisfaction (Diener, 1984, 1994; Diener and Larsen,1984; Pavot and Diener, 1993). Both components, although notorthogonal, are shown to be distinct from each other (Andrewsand Withey, 1976; Diener, 1994; Lucas et al., 1996). For example,Diener (1994) reported that the affective component tends tobecome easily influenced by physical states (e.g., pain, illness),whereas life satisfaction does not. Moreover, in a recent meta-analysis investigating the relationship between personality traits (asconceptualized by Costa and McRae’s (1992) five factor personalitystructure) and SWB, life satisfaction most strongly correlated withthe Conscientiousness factor, positive affect most strongly correl-ated with the Extraversion factor, and negative affect most stronglycorrelated with the Neuroticism factor (DeNeve and Cooper, 1998).Thus, given the degree of independence between affect and lifesatisfaction, it is suggested that studies investigating SWB shouldassess each component separately, rather than examine the constructas a monolithic entity (Diener et al., 1999).

However, although the affective and cognitive components ofSWB appear to be equally important, the former has been the subjectof most academic attention (Pavot and Diener, 1993). This focusis not surprising when one considers the ubiquity of emotions ineveryday life (DeNeve and Cooper, 1998). Nevertheless, the pre-ponderance of studies investigating the affective component hascome at the expense of investigating the cognitive aspects of SWB(i.e., life satisfaction). Life satisfaction refers to an individual’soverall cognitive appraisal of the quality of his/her life (Camp-bell et al., 1976; Diener, 1984; Shin and Johnson, 1978). Becausean individual’s outlook on life is based more on cognitive refer-ences than on emotional reactions to short-term events (Pavot andDiener, 1999), life satisfaction is conceptually considered to bemore stable than the affective component (Diener and Larsen, 1984),and is thus thought to be a key indicator of positive subjective

MSLSS AND ADOLESCENTS 137

well-being (Argyle, 1987; Diener and Diener, 1995). Therefore,given the unique contributions that life satisfaction reports add tothe nomothetic understanding of SWB, considerable attention hasnow begun to focus on assessing the nature and correlates of lifesatisfaction among individuals (Diener, 1994; Pavot and Diener,1993).

Most of the research investigating life satisfaction has beenconducted with adult and gerontological samples (Bender, 1997;Rutter, 1995). Few studies to date have directly investigated thisconstruct among children and adolescents (see Bender, 1997; Veen-hoven, 1993). This lack of research has important implicationsconsidering that the nature and determinants of life satisfactionmay not be equivalent for children and adults given the differentialstressors inherent in each population (Collins, 1991; Dusek, 1987).For example, Headey et al. (1991) reported that marital satisfac-tion significantly provided a significant influence in adults’ globallife satisfaction reports. Further, Tait, Padget, and Baldwin’s (1989)meta-analysis revealed a moderate correlation between job satisfac-tion and global life satisfaction. Neither of these domains would beconsidered to pertain to school-aged children. Therefore, additionalresearch investigating life satisfaction among children and adoles-cents must be conducted in order to determine the contributions ofspecific domains directly relevant to this age group.

One explanation for this dearth of research may be due to thelack of instruments that can be administered to this population. Oneexception is the Multidimensional Student Life Satisfaction Scale(MSLSS: Huebner, 1994). This instrument was designed to allowresearchers to garner a general assessment of children’s life satis-faction, in addition to providing a separate score on five domainsthought important in their lives (family, friends, school, living envi-ronment, and self). The specific domains were selected on the basisof a literature review, pilot studies with school age children, andprevious research (Huebner, 1991b). The five domains selected wereexpected to be relevant and important to school age children. Extantresearch that addressed the domains included such examples as:school (Epstein and McPartland, 1977), family (Henry et al., 1992),peers (Asher et al., 1984), self (Harter, 1989), and living environ-ment (Homel and Burns, 1989). Thus, the use of the MSLSS was


conceptualized to provide a multicontexutual profile of children’slife satisfaction, which might eventually contribute to more context-specific intervention efforts designed to promote children’s positivewell-being (Huebner, 1994; Huebner et al., 1998).

Studies of the MSLSS have consistently demonstrated accept-able psychometric properties with elementary and middle schoolstudents (Greenspoon and Saklofske, 1997; Huebner, 1994;Huebner et al., 1999; Huebner et al., 1998), providing prelim-inary support for use for research purposes. For example, reliabilitystudies of the MSLSS have consistently yielded alpha coefficientsjudged adequate for research purposes, with alphas ranging from0.90 to 0.92 for the Total score and 0.77 to 0.85 for the domainscores (Huebner and Gilman, 1998). Further, the stability coeffi-cients for each of the MSLSS subscales and the Total score haveranged from 0.78 to 0.90 (Huebner and Gilman, 1998).

Construct validity studies have also been promising. The under-lying factor structure of the MSLSS is considered to be hierarchicalin nature, with each specific life domain (e.g., friends, family, etc.)subsumed under a second-order general factor. Support for theunderlying model of the MSLSS has been demonstrated in a studyof middle school students (grades 6–8) using confirmatory factoranalytic procedures (Huebner et al., 1998).

Finally, evidence of the convergent and discriminant validity ofthe instrument has been demonstrated for the most part by patternsof correlations among the MSLSS domains and various criterionmeasures (e.g.,Self-Description Questionnaire-I(Marsh, 1990),theSelf-Description Questionnaire-II(Marsh, 1990), and theChil-dren’s Loneliness and Social Dissatisfaction Scale(Asher et al.,1984)). Further, although social desirability has traditionally beena matter of concern for self-report scales (Diener, 1994), discrim-inant validity research utilizing the MSLSS indicates only modestrelationships with this construct (e.g., Dew, 1996; Huebner et al.,1998).

To date, the psychometric properties of the MSLSS have beenstudied only in children from grades 3–8. No research has investi-gated the use of the MSLSS with older (i.e., high school) students.Because of the developmental changes associated with matura-tion, it would be inappropriate to assume the applicability of the


MSLSS with older adolescents. Rather, it is essential to empiric-ally validate the MSLSS with older adolescent samples. Based onprevious research related to childrens’ self-perceptions and happi-ness (e.g., Dew and Huebner, 1994; Huebner and Dew, 1996) andpilot investigations with adolescents, it was expected that the dimen-sions of the MSLSS would be meaningful to older adolescents aswell as younger students.

The purpose of the present study was thus to conduct a prelim-inary investigation of the psychometric properties of the MSLSSwith a sample of adolescents in grades 9–12. The specific aims of thestudy were fourfold. First, confirmatory factor analytic procedureswere utilized to assess the construct validity of the MSLSS withhigh school students. Second, convergent validity was assessed bycomparing four MSLSS domain scores with similar measures fromthe Behavioral Assessment Scale for Children (BASC: Reynoldsand Kamphaus, 1992). The MSLSS Living Environment domainwas not evaluated because a comparable scale is not included onthe BASC. Aside from its consistently strong psychometric prop-erties (Flanagan, 1995; Reynolds and Kamphaus, 1992), the choiceof the BASC was considered advantageous due to its incorporationof both negative (psychopathology-based) and positive (adaptivewell-being) scales. This allowed for a test of the hypothesis thatlife satisfaction scores would relate positively to the BASC positivewell-being scales and negatively to the BASC negative well-beingscales. Third, in addition to an exploration of its validity, the internalconsistency reliability of the MSLSS Total score as well as eachdomain score was calculated. Finally, the levels and demographiccorrelates (e.g., age, gender, race) of adolescent life satisfactionreports were explored.

METHOD

Subjects

A total of 515 students were solicited from two high schoolsfrom one school district in an urban area of a Southeastern state.The students were informed that only those who returned boththe student assent and parental consent form could participate inthe study. Of the 515 students who were originally solicited, 321


students returned both forms, resulting in a 62% return rate. Themean age of the sample was 16.14 (SD= 1.1). Females constituted65% of the sample. There were 48 ninth-graders, 55 tenth-graders,130 eleventh-graders, and 88 twelfth-graders. A total of 179 studentswere African-American, 136 were Caucasian, and 6 were classifiedas “other”. Socioeconomic status (SES) was assessed by asking eachstudent to report whether they received free/reduced or paid schoollunch services. A total of 167 students (55%) received free/reducedlunch, while 131 (45%) were on a regular lunch plan. Twenty-three students did not report their lunch status. Although the derivedsample was not representative of the U.S. population for students ofthis age group, the current sample accurately reflected the racial,gender, and socioeconomic characteristics of the school districtutilized in this study.

Measures

The Multidimensional Students’ Life Satisfaction Scale(MSLSS).The MSLSS is a 40-item self-report scale that examines the domainspecificity of life satisfaction while retaining a general life satis-faction rating. Respondents were asked to rate the extent of theirsatisfaction across the five domains of the MSLSS using one of thesix option choices: strongly disagree, moderately disagree, mildlydisagree, mildly agree, moderately agree, strongly agree. Thisparticular response format was different than the four-choice optiontypically used with elementary school children because adolescentsof this age were expected to be able to utilize the greater number ofcategories, providing greater variance in scores, and thus increasingthe sensitivity of the instrument (Huebner et al., 1998). Scoring ofthe MSLSS was determined by summing the ratings within eachdomain and dividing by the total number of items in that domain.In order to facilitate ease of interpretation, negatively-keyed itemson the instrument were reversed-scored so that a high score on theMSLSS indicated a high degree of satisfaction for the adolescent.As noted in the introduction, the MSLSS has yielded acceptablepsychometric properties across grades 3–8 (cf. Bender, 1997 for areview). The readability of the MSLSS is estimated to be at the1.5 grade level, therefore most students require little assistance inresponding to the questions (Huebner, 1994).


Behavior Assessment System for Children(BASC: Reynolds andKamphaus, 1992). The BASC is a 188-item multidimensional scalethat is appropriate for use with children and adolescents aged 4to 18 years. Each domain of the MSLSS can be assessed viaparent-, teacher-, and/or self-report. In this study, only the self-report protocol was utilized. The BASC measures 14 differentsubscales. These subscales can be divided into two basic categories:Clinical and Adaptive. The Clinical scales are comprised of thosethat represent negative or undesirable characteristics. Thus, highscores on these scales can be viewed as potential factors hinderingoptimal psychosocial and psychoeducational functioning. The Clin-ical scales of the BASC are: Anxiety, Attitude to School, Attitudeto Teachers, Atypicality, Depression, Locus of Control, SensationSeeking, Sense of Inadequacy, Social Stress, and Somatization.Conversely, the Adaptive scales reflect positive or desirable char-acteristics. Thus, elevated scores on the scales can be viewedas factors contributing to a student’s optimal social-emotionalfunctioning. The Adaptive scales of the BASC are: InterpersonalRelations, Relations with Parents, Self-Esteem, and Self-Reliance.Each item is responded to by the subject in a true/false format.The BASC demonstrates good internal consistency with coeffi-cient alphas ranging from 0.64 to 0.87 for the 14 facets. Factoranalytic techniques with the national normative sample supportedthe hypothesized factor structure of the instrument (Reynolds andKamphaus, 1992).

Procedure

After obtaining parent consent and student assent to participatein the study, the students assembled in their respective schoolcafeterias and were administered the test instruments by the firstauthor. The students were instructed to sit with atleast oneseat separating them from another student. Further, at least oneteacher/administrator was assigned a specific testing location inorder to monitor the students’ behavior. Finally, the instrumentswere presented in counterbalanced order. These strategies wereemployed in order to minimize social desirability and/or ordereffects. Prior to administration, the directions to each instrumentwere read aloud by the first author, who then remained throughout


the session in order to answer any questions that might arise. Aftercompleting the instruments, the students were debriefed and givenopportunities to ask questions.

RESULTS

Factor Structure of the MSLSS

Several confirmatory factor analytic analyses (CFAs) were run inorder to cross- validate the underlying hypothesized factor struc-ture of the MSLSS previously identified with younger children(Huebner, 1994; Huebner et al., 1998). In the present study, indi-vidual items of the MSLSS were used as observed variables fromwhich to measure their particular latent variable. However, theuse of individual items for tests of even moderate length oftenproduces results that are difficult to interpret. For example, Floydand Widaman noted that, “factor structures are difficult to determinewhen the measured variables are individual items from question-naires . . . especially if this means more than five to eight itemsare free to load on each latent variable” (1995, p. 293). Thus,a relatively common procedure is to conduct CFA procedures oncombined items (i.e., item parcels) rather than on individual items(Marsh et al., 1989; Marsh et al., 1998; Rindskopf and Rose, 1988).Although researchers have not agreed upon specific criteria for theconstruction of item parcels, in the present analyses item parcelswere created based on the following criteria. First, an individualitem comprising each parcel was not paired with the item withwhich it most strongly correlated. In this way, the possibility of“bloated specific” parcel contamination was minimized (Cattell,1978). Second, adjacent items in the MSLSS that measured thesame construct were not combined. This constraint was considerednecessary in order to avoid any priming effects (see Schwartz andStrack, 1991; Diener, 1994). Aside from these constraints, itemsparcels consisted of randomly selected MSLSS items from the samedomains.

Four parcels were thus constructed to represent the Friends,School, and Living Environment domains, and three parcels wereconstructed to represent the Family and Self domains of the MSLSS.In the majority of cases, the item parcels consisted of two individual


items. However, because the number of individual items variedacross each particular MSLSS scale, four item parcels consisted ofthree individual items. Item definitions of the parcels are shown inTable I. Correlations among the item parcels are shown in Table II.

For all of the CFA analyses, covariance matrices were fit usingthe maximum likelihood procedure in CALIS (SAS 6.11: SAS Insti-tute, 1990). In the present study, standardized estimates are reportedin order to facilitate ease in interpreting model parameters. Modelfit was established by examining a combination of absolute andincremental fit statistics. Absolute fit statistics index how well eachmodel tested in the analysis fit the current data. The absolute fitstatistics included the traditional chi-square (χ2) test of “exact”model fit, theχ2 test of “close” model fit (Browne and Cudek,1993), theχ2:degrees of freedom (df) ratio, the root-mean-squareerror of approximation (RMSEA: Steiger and Lind, 1980), and theGoodness-of-Fit Index (GFI). Incremental fit statistics were alsochosen for their ability to evaluate different aspects of model fit.Unlike absolute fit statistics which index the fit of the model inand of itself (Floyd and Widaman, 1995), incremental fit statisticscompare the relative fit of a proposed model against either a nullmodel or various related models (Hoyle and Painter, 1995). The twoincremental fit statistics chosen were the Tucker and Lewis Index(TLI: Tucker and Lewis, 1973) and the Comparative-Fit-Index (CFI:Bentler, 1989). For theχ2 tests, a significant value relative to thedegrees of freedom indicates that the model does not adequately fitthe data (Keith, 1999). Thus, a good fitting model is indicated bynon-significantresults from these tests. However, it has been notedthat χ2 tests are heavily influenced by sample size and are easyto reject as the sample size increases (Tanaka, 1993). In order toavoid this problem,χ2/df ratios were analyzed as well. In general,χ2/df ratios of less than 2 are considered to be indicators of goodfitting models (Crowley and Fan, 1997). For the GFI, TLI, and CFIindices “good” fitting models were indicated by a value of 0.90 ormore. Finally, RMSEA values of 0.05 or less were used to indicatea “good” fitting model.

Three different CFA models were fit in order to explore whetherthe underlying factor structure of the MSLSS could account forthe correlations found among the item parcels. The first model


TABLE I

Item Parcel Descriptions

MSLSS Item Parcel

My family gets along well together Family 1My parents and I do fun things together Family 1My parents treat me fairly Family 1My family is better than most Family 2Members of my family talk nicely to one another Family 2I like spending time with my parents Family 3I enjoy being at home with my family Family 3I wish I had different friends Friends 1I have enough friends Friends 1My friends are nice to me Friends 2I have a lot of fun with my friends Friends 2My friends are great Friends 3My friends treat me well Friends 3My friends will help me if I need it Friends 4I have a bad time with my friends Friends 4My friends are mean to me Friends 4I look forward to going to school School 1I enjoy school activities School 1I feel bad at school School 2I like being in school School 2There are many things about school I don’t like School 3School is interesting School 3I wish I didn’t have to go to school School 4I learn a lot at school School 4I wish I lived somewhere else Living Environment 1I like where I live Living Environment 1I like my neighborhood Living Environment 2I wish there were different people in my neighborhood Living Environment 2I wish I lived in a different house Living Environment 3I like my neighbors Living Environment 3There are lots of fun things to do where I live Living Environment 4This town is filled with mean people Living Environment 4My family’s house is nice Living Environment 4I am fun to be around Self 1I like myself Self 1I think I am good looking Self 2I am a nice person Self 2There are lots of things I can do well Self 3Most people like me Self 3I like to try new things Self 3

MS

LS

SA

ND

AD

OL

ES

CE

NT

S1

45

TABLE II

Correlations Among Parcels

Parcels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Family12 0.673 0.68 0.54Friends4 0.18 0.10 0.175 0.24 0.20 0.24 0.476 0.20 0.15 0.19 0.49 0.747 0.26 0.17 0.21 0.48 0.63 0.64School8 0.30 0.27 0.41 0.14 0.24 0.26 0.159 0.31 0.28 0.39 0.11 0.21 0.18 0.20 0.6910 0.15 0.12 0.22 0.02 0.10 0.09 0.06 0.57 0.5911 0.27 0.30 0.37 0.13 0.16 0.17 0.14 0.63 0.64 0.57Living Environment12 0.40 0.33 0.30 0.21 0.16 0.18 0.17 0.14 0.20 0.17 0.1913 0.39 0.28 0.31 0.17 0.17 0.18 0.15 0.14 0.21 0.16 0.21 0.7014 0.38 0.30 0.26 0.22 0.18 0.22 0.22 0.09 0.15 0.05 0.13 0.56 0.5015 0.41 0.33 0.29 0.11 0.16 0.14 0.10 0.22 0.25 0.18 0.23 0.51 0.50 0.42Self16 0.41 0.39 0.35 0.23 0.45 0.38 0.35 0.31 0.29 0.07 0.22 0.22 0.23 0.28 0.3517 0.29 0.28 0.28 0.30 0.34 0.31 0.37 0.22 0.23 0.01 0.20 0.14 0.16 0.22 0.26 0.7018 0.33 0.39 0.34 0.29 0.46 0.46 0.40 0.36 0.28 0.11 0.26 0.15 0.14 0.22 0.32 0.70 0.58


TABLE III

Model Fit Statistics for each Hypothesized Factor Model

df χ2 (exact) p (close) χ2/df RMSEA GFI TLI CFI

Hypothesized Model 1

125 217.96 0.60 1.74 0.05 0.93 0.96 0.97

Restricted Hypothesized Model 2: No Factor Correlations

135 529.36 0.00 3.09 0.10 0.83 0.84 0.86

Restricted Hypothesized Model 3: Single Second-Order Factor

130 258.24 0.17 1.99 0.06 0.92 0.95 0.95

was the least restrictive of the three (Hypothesized Model 1). Inthis model, a path was allowed between each item parcel andits hypothesized domain. Further, all domains were allowed tocorrelate with each other. Models 2 and 3 were more restrictedversions of this more generalized hypothesized model (i.e., nestedmodels). In the second model (Restricted Hypothesized Model2), no correlations were allowed among the five domains of theMSLSS. Although this model was not expected to adequately fitthe data, it was felt important to explicitly test whether an ortho-gonal domain model conceptualization could be ruled out. Finally,previous CFA procedures using the MSLSS with middle schoolstudents revealed a second-order factor structure in which eachlife satisfaction domain was subsumed under a more general factor(Huebner et al., 1998). Thus, in order to test whether a hierarchicalfactor structure also existed among older adolescents, a third model(Restricted Hypothesized Model 3) was constructed.

Model fit statistics are reported in Table III. For the Hypothe-sized Model 1, all absolute and incremental fit statistics clearly metthe criteria for a good fitting model. Even theχ2 tests, which havebeen noted to be susceptible to rejection as sample sizes increase,were not rejected in this model. Further, an examination of the pathcoefficients revealed that all item parcels served as valid indicatorsof their particular construct (see Figure 1). The mean parcel loading


Figure 1.

for each MSLSS domain was 0.80 for Family, 0.76 for Friends, 0.78for School, 0.74 for Living Environment, and 0.82 for Self. Further,the intercorrelations among the MSLSS domains were found tobe moderate, ranging from 0.27 (School, Friends) to 0.57 (Self,Friends), with a mean interfactor correlation of 0.38. This moderatecoefficient indicated that the MSLSS domains, while related, werenevertheless separable from each other.

In contrast,noneof the fit statistics for the Restricted Hypothe-sized Model 2 met the criterion for a good fitting model. Thus, theconception that the domains of the MSLSS were entirely orthogonalwas rejected.

Finally, the fit statistics for the Restricted Hierarchical Model3 also revealed a very good fit to the data. In addition to the fit


Figure 2.

statistics, the path coefficients from each MSLSS domain to thesecond-order factor ranged from 0.52 (School) to 0.74 (Family)(see Figure 2). These coefficients were very similar to those foundin previous studies using the MSLSS with younger adolescents(Huebner et al., 1998), suggesting that a higher-order general factorstructure of the instrument exists with older adolescents as well.

Convergent and Discriminant Validity

The correlational matrix of the MSLSS and BASC scales is reportedin Table IV. Convergent and discriminant validity were evaluated intwo ways. First, on a more informal level, the following pattern ofcorrelations were expected after visually inspecting the correlationalmatrix: 1) significant and higher correlations among the corres-ponding MSLSS and BASC scales, and 2) much lower correlationsamong the noncorresponding scales. Specifically, higher correla-tions were expected between: 1) the MSLSS School scale and theBASC Attitude Toward School scale, 2) the MSLSS Family scale


TABLE IV

Correlation Matrix of the MSLSS and the BASC Scales (N = 321)

Total Family Friends School Living Self

Environment

BASC Adaptive ScalesParental Relations 0.55 0.61 0.27 0.29 0.31 0.32

Interpersonal

Relations 0.36 0.23 0.39 0.23 0.14 0.38

Self-Esteem 0.44 0.27 0.26 0.18 0.25 0.56

Self-Reliance 0.32 0.26 0.22 0.20 0.12 0.33

BASC Clinical ScalesAttitude Towards

School –0.45 –0.32 –0.14 –0.70 –0.20 –0.17

Attitude Towards

Teachers –0.36 –0.24 –0.15 –0.40 –0.23 –0.14

Sensation Seeking –0.18 –0.22 –0.02 –0.22 –0.11 0.00

Atypicality –0.40 –0.36 –0.17 –0.20 –0.29 –0.21

Locus of Control –0.47 –0.41 –0.27 –0.17 –0.34 –0.29

Somatization –0.29 –0.23 –0.15 –0.18 –0.22 –0.22

Social Stress –0.52 -0.34 –0.38 –0.18 –0.40 –0.43

Anxiety –0.32 –0.26 –0.16 0.00 –0.29 –0.29

Depression –0.61 –0.43 –0.32 –0.29 –0.41 –0.54

Sense of Inadequacy –0.48 –0.33 –0.24 –0.37 –0.30 –0.34

The validity coefficients are noted in bold print.

and the BASC Relations with Parents scale, 3) the MSLSS Selfscale and the BASC Self-Esteem scale, and 4) the MSLSS Friendsscale and the BASC Interpersonal Relations scale. Second, on amore formal level, a canonical correlation analysis was conductedbetween the MSLSS and the BASC in order to identify the strongestpatterns of correlations across the two instruments. These correla-tional patterns were investigated in order to assess the degree ofconvergence between similar MSLSS and BASC constructs.

The informal evaluation of the correlation matrix clearly showsthe predicted pattern of high correlations among MSLSS and BASC


TABLE V

Canonical Correlation Results

Cumulative Percentage

of Variance

Canonical F Num Den p MSLSS BASCPair df df

1 11.36 70 1347 0.0001 27 18

2 7.46 52 1098 0.0001 36 25

3 5.24 36 840 0.0001 39 26

4 3.09 22 570 0.0001 41 27

5 2.39 10 286 0.0097 42 28

scales. Specifically, the MSLSS School scale correlated highest withthe BASC Attitude Toward School Scale (r = –0.70), the MSLSSFamily scale correlated highest with the BASC Parental Relationsscale (r = 0.61), the MSLSS Self scale correlated highest withthe BASC Self-Esteem scale (r = 0.56), and the MSLSS Friendsscale correlated highest with the BASC Interpersonal Relationsscale (r = 0.39). The smaller magnitude of this latter correlationlikely reflects the fact that the BASC Interpersonal Relations scaleis not limited to perceptions of the quality of friendships (as isthe case for the MSLSS Friends domain), but includes perceptionsof relationships with others, including adults and non-friend peers.Additional evidence for the discriminant validity of the MSLSSdomain scales was obtained by the differential correlations betweenconceptually dissimilar scales. Specifically, modest to moderateinverse correlations were typically obtained between the MSLSSscales and the BASC Clinical scales, whereas modest to moderatepositive correlations were obtained among the MSLSS and BASCAdaptive scales.

The results of the canonical correlation analysis are summa-rized in Table V. All five possible canonical variate pairs reflectedsignificant patterns of correlations between the MSLSS and BASCscales. Further, roughly 42% of the variation of the MSLSS scaleswas significantly related to the variation in the BASC scales. To aidin the interpretation of the correlational patterns, the five significant


canonical variates were rotated following the procedures recom-mended by Cliff and Krus (1976). The canonical structure for therotated variate pairs is presented in Table VI. Clearly, the correla-tional patterns reflected in the first four canonical variates mirrorsthe more informal visual inspection of the correlational matrix, andprovides further support for the construct validity of the MSLSS.Specifically, by canonical variate pair: 1) the MSLSS School scalemost strongly related to the BASC Attitude Towards School andAttitude Towards Teachers scales, 2) the MSLSS Family scale moststrongly related to the BASC Relations with Parents scale, 3) theMSLSS Self scale most strongly related to the BASC Self-Esteemscale (although higher correlations with the Reliance, Depression,and Interpersonal Relationships were also reflected), and 4) theMSLSS Friends scale most strongly correlated with the BASCInterpersonal Relationships scale. Further, the MSLSS Living Envi-ronment scale, captured in the fifth canonical variate, showed theweakest pattern of correlation with the BASC.

Finally, the relationship between the MSLSS Total score andBASC scales was investigated. Similar to the findings obtainedwith the MSLSS domain scores, the Total score was moderatelypositively related to the BASC Adaptive scales, and negativelycorrelated with the BASC clinical scales. Further, the Total scorerelated most strongly to the BASC Depression scale (r = –0.61).This finding was similar to previous studies (e.g., Huebner andAlderman, 1993) and provided some evidence for the validity ofthe Total score.

Reliability

The coefficient alphas (based on the entire sample) for each domainof the MSLSS are reported in Table VII. The internal consistencyestimate of the Total score was 0.91, suggesting high overall consist-ency of the MSLSS items. This coefficient paralleled that foundamong American elementary and middle school students (Huebner,1994; Huebner et al., 1998) and Canadian students (Greenspoonand Saklofske, 1997). The coefficient alpha for each MSLSS scaleranged from 0.86 (Family) to 0.79 (Living Environment) andparalleled those found in previous research (Huebner et al., 1998).All coefficient alphas were judged to be acceptable for research


TABLE VI

Canonical Structure for Rotated Canonical Variates

Structure for Canonical Variate Pair

MSLSS 1 2 3 4 5

School 0.97 0.15 0.12 0.08 0.10

Family 0.18 0.93 0.19 0.10 0.23

Self 0.13 0.19 0.93 0.24 0.12

Friends 0.08 0.10 0.21 0.96 0.09

Living Environment 0.11 0.21 0.12 0.09 0.96

BASC

Attitude Towards School 0.93 –0.25 0.02 –0.03 0.11

Attitude Towards Teacher 0.57 –0.31 –0.05 –0.18 0.19

Relations With Parents –0.13 0.90 0.15 0.26 –0.18

Self-Esteem 0.06 0.20 0.79 0.05 –0.45

Reliance on Others –0.13 0.37 0.55 0.19 0.24

Depression 0.15 –0.30 –0.55 –0.23 0.53

Interpersonal Relations –0.18 0.11 0.51 0.70 0.07

Stress –0.01 –0.17 –0.30 –0.52 0.70

Anxiety –0.20 –0.24 –0.30 –0.04 0.57

Locus of Control 0.00 –0.46 –0.05 –0.37 0.52

Atypicality 0.13 –0.40 –0.05 –0.11 0.46

Inadequacy 0.37 –0.21 –0.28 –0.19 0.35

Somaticism 0.13 –0.23 –0.25 –0.15 0.24

Sensation Seeking 0.28 –0.30 0.18 0.13 0.18

purposes, based on the criterion suggested by Salvia and Ysseldyke(1988).

Descriptive Statistics of the MSLSS

The subscales means and standard for each MSLSS domain are alsoreported in Table VII. The mean score for the Family, Friends, Self,and Total domains corresponded to the response of “moderatelyagree”. Mean scores for the School and Living Environment scalescorresponded to the response option of “mildly agree”. These rela-


TABLE VII

Descriptive Data and Internal Consistency Estimates of theMSLSS Total and Domain Scores for the Entire Sample (N =321).

Domain Mean SD Coefficient Alpha

Total 4.35 0.65 0.91

Family 4.25 1.09 0.86

Friends 5.12 0.73 0.82

School 3.74 1.05 0.84

Living Environment 3.68 1.03 0.79

Self 5.14 0.79 0.84

tively positive ratings are consistent with previous studies of adults(e.g., Diener and Diener, 1996), middle school students (Huebneret al., 1999), and elementary school students (e.g., Huebner et al.,1998).

The relationships among demographic variables and life satis-faction domains were explored through the use of an omnibusMANOVA test. This test was chosen over a series of ANOVAtests in order to control for inflated type 1 errors. The demo-graphic variables chosen for examination were age, grade, gender,race, and SES. Further, because the Total score is comprised ofthe total of all MSLSS items, separate statistical analyses for theoverall life satisfaction score were conducted in order to avoidambiguous interpretations of the results. A series of Bonferroni-corrected ANOVAs were thus computed for the MSLSS Total scoreand each demographic variable.

Using Wilks’ Lambda, significant main effects were noted forgender (F(5, 309) = 3.36,p < 0.01), race (F(5, 309) = 8.13,p < 0.01) and socioeconomic status (F(5, 292) = 3.59,p < 0.01).No significant differences were found for age and grade. Univariatepairwise comparisons were then analyzed in order to determinewhich domain contributed significantly to the overall effect. In addi-tion, effect sizes were also computed in order to determine themagnitude of the difference. Significant main effects were notedfor gender on the Friends domain (F(1, 313) = 11.73,p < 0.001,


ES= 0.43), with females reporting higher levels of satisfaction thanmales. Further, significant main effects for race were noted on theFamily domain (F(1, 313) = 6.13,p < 0.001,ES= 0.30), the Selfdomain (F(1, 313) = 30.79,p < 0.001,ES= 0.69), and the Totalscore (F(1, 313) = 9.24,p < 0.001, ES = 0.39), with African-American respondents reporting higher levels of life satisfactionacross these domains than their Caucasian peers. Finally, signi-ficant effects for socioeconomic status were observed on the Selfdomain (F(1, 296) = 5.97,p < 0.001,ES= 0.30), with lower SESrespondents reporting higher satisfaction scores than higher SESstudents. With the exception of the race difference on the MSLSSSelf domain, the moderate effect sizes indicate that the influenceof demographic variables on adolescent life satisfaction reports isrelatively modest in terms of practical significance.

DISCUSSION

In general, preliminary psychometric support for the use of theMSLSS as a research tool with older adolescent (i.e., high school)students was indicated. Specifically, evidence for the constructvalidity of the MSLSS was obtained via confirmatory factor analysissupporting the hypothesized hierarchical multidimensional model.The path coefficients and overall fit statistics were very similar to thehierarchical model previously confirmed with younger adolescentstudents (Huebner et al., 1998). Further, the pattern of factor load-ings was almost identical with that of younger children (see Huebneret al., 1998), suggesting that the hierarchical factor structure remainsinvariant with respect to age. Finally, the presence of a higher-ordergeneral factor demonstrated the usefulness of the Total score whenassessing life satisfaction among older adolescents.

Support for the convergent and discriminant validity of theMSLSS was also indicated in that the four selected domains relatedmeaningfully to targeted scales on the BASC. This finding wasconsistent across both informal (i.e., visual inspection of the overallcorrelational matrix) and formal (i.e., canonical correlation analysis)levels of data investigation. Additional evidence for the validityof the instrument was provided by consistent positive correla-tions between the MSLSS and BASC Adaptive scales and negative


correlations between the MSLSS and BASC Clinical scales. Theselatter results were obtained for the Total score and domain scores ofthe MSLSS.

The internal consistency coefficients of the MSLSS Total scoreand domain scores all yielded adequate estimates. The coefficientalpha for the Total score surpassed standards for research purposesand was higher than that reported for many adult scales (cf. Andrewsand Robinson, 1991; Okun and Strock, 1987; Pavot and Diener,1993), perhaps because of the relatively greater number of itemscomprising the MSLSS. Further, the internal consistency reliabilityof each MSLSS domain was acceptable for research purposes. Eventhe Self scale, which has previously yielded a lower reliability coef-ficient than the other domains of the MSLSS (e.g., Greenspoon andSaklofske, 1997; Huebner, 1994) exceeded the criterion acceptablefor research purposes (Salvia and Ysseldyke, 1988).

The results of the present study also indicated that older adoles-cents reported positive levels of life satisfaction across all domainsof the MSLSS. Such findings of high levels of life satisfaction areconsistent other studies of younger students (e.g., Huebner, 1994;Huebner at al., 1998), adolescents (Huebner et al., 1999), and adults(Argyle, 1987; Diener, 1984, 1994).

The relationships among life satisfaction reports and selecteddemographics (age, grade, gender, race, and SES) were also investi-gated. With one exception, the results revealed modest relationshipsat best, suggesting that demographic variables play a relativelyminor role in adolescents’ life satisfaction. Overall, such find-ings are consistent with studies of adults (Andrews and Robinson,1991; Diener, 1994) and children (Huebner, 1997), and adolescents(Huebner et al., 1999). The findings that race and self-satisfactionwere moderately related in this sample is inconsistent with findingsfrom other studies of adolescents (Dew, 1996; Huebner et al., inpress) and younger students (Huebner, 1994), and thus merits furtherinvestigation of its generalizability.

Although the current study provided preliminary psychometricsupport for the use of the MSLSS with older (i.e., high school)students, limitations related to the generalizability of the find-ings should be underscored. The most serious possible limitationinvolves the representativeness of the sample. Specifically, the parti-


cipants were disproportionally female and African-American anddrawn from one school district in one Southeastern state. Given theaforementioned modest influence of the demographic variables onadolescent life satisfaction reports in this study and others (e.g.,Huebner et al., in press), concerns related to race and gender repre-sentation may be tempered. Nevertheless, future research effortswith the MSLSS would benefit from more representative samples,including students from other parts of the United States and otherethnic groups (e.g., Asian Americans, Mexican Americans, etc.) aswell.

ACKNOWLEDGEMENTS

The authors would like to extend their deep appreciation to thefaculty and administration of Lakewood and Crestwood HighSchools in Sumter, South Carolina for their assistance in this project.

REFERENCES

Andrews, F. M. and J. P. Robinson: 1991, ‘Measures of subjective well-being’, inJ. P. Robinson, P. R. Shaver and L. S. Wrightman (eds.), Measures of Personalityand Social Psychological Attitudes (Vol. 1). (Academic Press, San Diego, CA).

Andrews, F. M. and S. B. Withey: 1976, ‘Social Indicators of Well-Being:America’s Perception of Life Quality’ (Plenum Press, New York).

Annas, J.: 1993, ‘The Morality of Happiness’ (Oxford University Press, NewYork).

Argyle, M.: 1987, ‘The Psychology of Happiness’ (Routledge, London).Asher, S. R., S. Hymel, and P. D. Renshaw: 1984, ‘Loneliness in children’, Child

Development 55, pp. 1456–1464.Bender, T. A.: 1997, ‘Assessment of subjective well-being in children and

adolescents’, In G. Phye (ed.), Handbook of Classroom Assessment: Learning,Achievement, and Adjustment (Academic Press, San Diego, CA).

Basche, M. F.: 1995, ‘Doing Brief Psychotherapy’ (Basic Books, New York).Bentler, P. M.: 1994, ‘Forward’, in B. M. Byrne (ed.), Structural Equation

Modeling with EQS and EQS/Windows (Sage, Thousand Oaks, CA), pp. vii–ix.Browne, M. W. and R. Cudek: 1993, ‘Alternative ways of assessing model fit’,

in K. A. Bollen and J. S. Long (eds.) Testing Structural Equation Models(Newbury Park, Sage), pp. 136–162.

Cliff, N. and D. J. Krus: 1976, ‘Interpretation of canonical analysis: Rotated vs.unrotated solutions’, Psychometrika 41, pp. 35–42.


Costa, P. T. and R. R. McCrae: 1992, Revised NEO Personality Inventory (NEOPI-R) and NEO Five-Factor Inventory Professional Manual’ (Odessa, FL:Psychological Assessment Resources).

Cowen, E.: 1991, ‘In pursuit of wellness’, American Psychologist 46, pp. 404–408.

Cowen, E.: 1994, ‘The enhancement of psychological wellness: Challenges andopportunities’, American Journal of Community Psychology 22, pp. 149–179.

Crowley, S. L. and X. Fan: 1997, ‘Structural equation modeling: Basic conceptsand applications in personality assessment research’, Journal of PersonalityAssessment 68, pp. 508–531.

DeNeve, K. M. and H. Cooper: 1998, ‘The happy personality: A meta-analysis of137 personality traits and subjective well-being’, Psychological Bulletin 124,pp. 197–229.

Dew, T.: 1996, ‘The preliminary development and validation of a multidimen-sional life satisfaction scale for adolescents’, Unpublished doctoral dissertation(University of South Carolina, Columbia).

Diener, E.: 1984, ‘Subjective well-being’, Psychological Bulletin 95, pp. 542–575.

Diener, E.: 1994, ‘Assessing subjective well-being: Progress and opportunities’,Social Indicators Research 31, pp. 103–157.

Diener, E., E. M. Suh, R. E. Lucas, and H. L. Smith: 1999, ‘Subjective well-being:Three decades of progress’, Psychological Bulletin 125, pp. 276–302.

Diener, E. and M. Diener: 1995, ‘Cross-cultural correlates of life satisfaction andself-esteem’, Journal of Personality and Social Psychology 68, pp. 653–663.

Diener, E. and R. J. Larsen: 1984, ‘Temporal stability and cross-cultural consist-ency of affective, behavioral, and cognitive responses’, Journal of Personalityand Social Psychology 47, pp. 871–883.

Durlak, J. A. and A. M. Wells: 1997, ‘Primary prevention mental health programsfor children and adolescents: A meta-analytic review’, American Journal ofCommunity Psychology 25, pp. 115–152.

Epstein, J. and J. M. McPartland: 1977, ‘The Quality of School Life Scale’,(Riverside, Riverside, CA).

Floyd, F. J. and K. F. Widaman: 1995, ‘Factor analysis in the development andrefinement of clinical assessment instruments’, Psychological Assessment 7,pp. 286–299.

Frisch, M. B., J. Cornell, M. Villenueva and R. J. Retzlaff: 1992, ‘Clinical valid-ation of the Quality of Life Inventory: A measure of life satisfaction for usein treatment planning and outcome assessment’, Psychological Assessment 4,pp. 92–101.

Furr, R. M. and D. C. Funder: 1998, ‘A multimodal analysis of personalnegativity’, Journal of Personality and Social Psychology 74, pp. 1580–1591.

Greenspoon, P. J. and D. H. Saklofske: 1997, ‘Validity and reliability of the Multi-dimensional Students’ Life Satisfaction Scale with Canadian children’, Journalof Psychoeducational Assessment 15, pp. 138–155.


Harter, S.: 1989, ‘Causes, correlates, and the functional role of global self-worth:A life span perspective’, In J. Kolligan and R. Sternberg (eds.), Perceptions ofCompetence and Incompetence Across the Life Span (Yale University Press,New Haven, CT), pp. 67–100.

Headey, B., R. Veenhoven, and A. Wearing: 1991, ‘Top-down versus bottom-uptheories of subjective well-being’ Social Indicators Research 24, pp. 81–100.

Henry, C. S., D. L. Ostrander, and S. G. Lovelace: 1992, ‘Reliability and validityof the Adolescent Family Life Satisfaction Index’, Psychological Reports 70,pp. 1223–1229.

Homel, R. and A. Burns: 1989, ‘Environmental quality and well-being ofchildren’, Social Indicators Research 21, pp. 133–158.

Hoyle, R.H. and A. T. Painter: 1995, ‘Writing about structural equation models’,in R. H. Hoyle (ed.), Structural Equation Modeling: Concepts, Issues, andApplications (Sage, Thousand Oaks, CA).

Huebner, E. S.: 1991b, ‘Correlates of life satisfaction in children’, SchoolPsychology Quarterly 6, pp. 103–111.

Huebner, E. S.: 1994, ‘Preliminary development and validation of a multidi-mensional life satisfaction scale for children’, Psychological Assessment 6,pp. 149–158.

Huebner, E. S.: 1997, ‘Happiness and life satisfaction’, in G. Bear, K. Minkeand A. Thomas, (eds.), Children’s Needs II. (National Association of SchoolPsychologists, Washington, DC), pp. 271–278.

Huebner, E. S.and G. Alderman: 1993, ‘Convergent and discriminant validity ofa children’s life satisfaction scale: Its relationship to self- and teacher-reportedpsychological problems and school functioning’, Social Indicators Research 30,pp. 71–82.

Huebner, E. S. and T. Dew: 1996, ‘The interrelationship of positive affect,negative affect, and life satisfaction in an adolescent sample’, Social IndicatorsResearch 38, pp. 129–137.

Huebner, E. S. and R. Gilman: 1998, ‘Children’s perception of the quality of theirlives: A neglected component in the psychoeducational assessment of children’swell-being’, Paper presented at the annual meeting of the National Associationof School Psychologists, Orlando Fl.

Huebner, E. S., W. Drane, and R. F. Valois: in press, ‘Adolescents’ perceptions oftheir quality of life’, School Psychology International .

Huebner, E. S., R. Gilman, and J. E. Laughlin: 1999, ‘A multimethod investiga-tion of the multidimensionality of children’s well-being reports: Discriminantvalidity of life satisfaction and self-esteem’, Social Indicators Research 46,pp. 1–22.

Huebner, E. S., J. E. Laughlin, C. Ash and R. Gilman: 1998, ‘Further valid-ation of the Multidimensional Students’ Life Satisfaction Scale’, Journal ofPsychoeducational Assessement 16, pp. 118–134.

Kamman, R., M. Farry, and P. Herbison: 1984, ‘The analysis and measurement ofhappiness as a sense of well-being’, Social Indicators Research 15, pp. 91–116.


Keith, T. Z.: 1999, ‘Structural equation modeling in school psychology’, in C. R.Reynolds and T. B. Gutkin (eds.), Handbook of School Psychology (3rd ed.)(Wiley, New York), pp. 78–107.

Lewinsohn, P., J. Redner, and J. Seeley: 1991, ‘The relationship between lifesatisfaction and psychosocial variables: New perspectives’, in F. Strack, M.Argyle and N. Schwartz (eds.), Subjective Well-Being (Plenum Press, NewYork), pp. 141–169.

Lucas, R. E., E. Diener and E. Suh: 1996, ‘Discriminant validity of well-beingmeasures’, Journal of Personality and Social Psychology 71, pp. 616–628.

McCullough, G., E. S. Huebner and J. E. Laughlin: 1999, ‘Life events, self-concept, and positive well-being’, Manuscript submitted for publication.

Marsh, H. W.: 1990, ‘Manual for the Self-Description Questionnaire-I’ (Univer-sity of Western Sydney, Campbelltown, NSW, Australia).

Marsh, H. W.: 1990, ‘Manual for the Self-Description Questionnaire-II’ (Univer-sity of Western Sydney, Campbelltown, NSW, Australia).

Marsh, H. W., J. K. Antill, and J. D. Cunningham: 1989, ‘Masculinity, femininity,and androgyny: Bipolar and independent constructs’, Journal of Personality 57,pp. 625–663.

Marsh, H.W., K. T. Hau, J. R. Balla, and D. Grayson: 1998, ‘Is more ever toomuch? The number of indicators per factor in confirmatory factor analysis’,Multivariate Behavioral Research 33, pp. 181–220.

Okun, M. A. and W. A. Strock: 1987, ‘The construct validity of subjective well-being measures: As assessment via quantitative research syntheses’, Journal ofCommunity Psychology 15, pp. 481–492.

Pavot, W. and E. Diener: 1993, ‘Review of the Satisfaction with Life Scale’,Psychological Assessment 5, pp. 164–172.

Reynolds, C.R. and R. W. Kamphaus: 1992, ‘Manual for the Behavioral Assess-ment System for Children’, (American Guidance Service, Inc., Circle Pines,MN).

Rindskopf, D. and T. Rose: 1988, ‘Some theory and applications of confirmatorysecond-order factor analysis’, Multivariate Behavioral Research 23, pp. 51–67.

SAS Institute, Inc.: 1990, ‘The CALIS procedure: Analysis of CovarianceStructures’, (Author, Cary, NC).

Salvia, J. and J. E. Ysseldyke: 1988, ‘Assessment in Special and RemedialEducation (3rd ed.)’, (Houghton Mifflin, Boston, MA).

Sandvik, E., E. Diener, and L. Siedlitz: 1993, ‘The assessment of well-being: Acomparison of self-report and nonself-report strategies’, Journal of Personality61, pp. 317–342.

Shinn, D.C. and D. M. Johnson: 1978, ‘Avowed happiness as an overall assess-ment of the quality of life’, Social Indicators Research 5, pp. 395–492.

Steiger, J. H. and J. C. Lind: 1980, May, ‘Statistically based tests for the numberof factors’, Paper presented at the annual meeting of the Psychometric Society,Iowa City, IA.

Stevens, J.: 1996, Applied Multivariate Statistics for the Social Sciences (3rd ed.)(Lawrence Erlbaum Associates, Mahwah, NJ).


Strack, F., M. Argyle, and N. Schwartz (Eds.): 1991, ‘Subjective Well-Being: AnInterdisciplinary Perspective’, (Pergamon Press, Oxford).

Tait, M., M. Y. Padget, and T. T. Baldwin: 1989, ‘Job and life satisfaction: Areexamination of the strength of the relationship and gender effects as a functionof the date of study’, Journal of Applied Psychology 34, pp. 502–507.

Tanaka, J. S.: 1993, ‘Multifaceted conceptions of fit in structural equationmodels’, in K. A. Bollen and J. S. Long (eds.), Testing Structural EquationModels (Sage, Newbury Park, CA), pp. 10–39.

Tucker, L. R. and C. Lewis: 1973, ‘A reliability coefficient for maximumlikelihood factor analysis’, Psychometrika 38, pp. 1–10.

Veenhoven, R.: 1996, ‘Developments in satisfaction research’, Social IndicatorsResearch 37, pp. 1–46.

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Documents

A First Study of the Multidimensional Students’ Life Satisfaction Scale with Adolescents