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    1/31 Counseling Psychologist online version of this article can be found at:

    DOI: 10.1177/00110000042722602005 33: 269The Counseling Psychologist

    Matthew P. MartensThe Use of Structural Equation Modeling in Counseling Psychology Research

    Published by:

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    Division of Counseling Psychology of the American Psychological Association

    can be found at:The Counseling PsychologistAdditional services and information for Alerts:

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    10.1177/0011000004272260THE COUNSELING PSYCHOLOGIST / May 2005Martens / SEM IN COUNSELING PSYCHOLOGYThe Use of Structural Equation Modeling in

    Counseling Psychology Research

    Matthew P. MartensUniversity at Albany, State University of New York

    Structural equation modeling (SEM) has become increasingly popular for analyzing

    data in the social sciences, although several broad reviews of psychology journals sug-

    gest that many SEM researchers engage in questionable practices when using the tech-

    nique. The purpose of this study is to review and critique the use of SEM in counseling

    psychologyresearchregardingseveral of these questionablepractices. One hundredfive

    studies from 99 separate articles published in the Journal of Counseling Psychology

    between1987and2003werereviewed.Results of thereview indicate that many counsel-

    ing psychology studies do not engage in various best practices recommended by SEM

    experts (e.g., testingmultiple a priori theoreticalmodels or reportingall parameteresti-

    mates or effect sizes). Results also indicate that SEMpractices in counseling psychology

    seem to be improving in some areas, whereasin other areasno improvements were noted

    over time. Implications of these results are discussed, and suggestions for SEM use

    within counseling psychology are provided.

    Structural equation modeling (SEM) is a techniqueforanalyzingdata that

    is designed to assess relationships among both manifest (i.e., directly mea-

    sured or observed) and latent (i.e., theunderlying theoretical construct) vari-

    ables. When using statistical techniques such as multiple regression or

    ANOVA, the researcher only conducts his or her analysis on variables that

    are directly measured, which can be somewhat limiting when the individual

    is interested in testing underlying theoretical constructs. For example, in an

    ANOVA design, a researcher interested in studying the construct of depres-sionmight include oneself-report depression scaleas thedependentvariable.

    The researcher may interpret that scale as representative of the entire con-

    struct of depression, a dubious conclusion given the complexity of depres-

    sion. In contrast, a researcher using SEM could explicitly model the latent

    construct of depression rather than relying on one variable as a proxy for the

    construct. SEM also provides advantages over other data analytic techniques

    in that complex theoretical models can be examined in one analysis.1


    I thank Richard Haase, TiffanySanford,and SamuelZizzi fortheirwork on earlier draftsof this

    article and Kirsten Corbett, Amanda Ferrier, Melissa Sheehy, and Xuelin Weng for their help in

    coding thedata. A previousversionof thisarticlewas presentedat the2003annualmeetingofthe

    American Psychological Association. Correspondence concerning this article should be

    addressed to Matthew P. Martens, Departmentof Educational and Counseling Psychology, Uni-

    versity at Albany, State University of New York, ED220, 1400 Washington Ave, Albany, NewYork 12222; phone: (518) 442-5039; e-mail:

    THE COUNSELING PSYCHOLOGIST, Vol. 33 No. 3, May 2005 269-298

    DOI: 10.1177/0011000004272260

    2005 by the Society of Counseling Psychology

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    A hypothetical example of a structural equation model that illustrates

    some advantages of SEM is presented in Figure 1.2 This model includes five

    latent constructs that are represented by ovals: personality characteristics

    thought to be associated with alcohol use (personality), familial factors

    thought to be related to alcohol use (family risk), motivations for using

    alcohol (drinking motives), strategies that canbe used to limit alcohol con-

    sumption and problems related to alcohol use (protective behaviors), and

    problems associated with alcohol consumption (alcohol problems). Each

    latent variable includes several measured indicator variables, represented by

    rectangles, that are thought to represent components of the underlying vari-

    able. Therefore, one can see how the researcher can explicitly model the

    underlying constructs of interest via SEM by directly incorporating the

    constructs into the model that is to be tested.Figure 1 also demonstrates a relatively complex series of relationships

    thatexplainor predict problems associated withalcohol consumption, which

    would then be testedin a singleanalysis. In this model,both personalitychar-

    acteristics and family risk factors are thought to predict or cause motivation

    fordrinking anduseof protectivebehaviors, which arethen in turn thought to

    predict or cause alcohol-related problems. These causal paths are indicated

    by single-headed arrows between the variables in question (note that such

    pathsexist between eachlatent constructand itsobservedindicatorvariables,

    which occurs because the latent construct is thought to cause whatever

    responses occur in the observed variables that represent the construct). Per-

    sonality characteristics and family risk factors are conceptualized as being

    correlated, but no causal or predictive relationship is specified. Therefore, a

    double-headed curved arrow indicates the relationship between these twoconstructs, which represents covariance between variables.

    As Figure 1 illustrates, SEM is well suited for model testing because the

    researcher can specify causal models that correspond to a theoretical per -

    spective. Through SEM the researcher can then test the plausibility of the

    modelson observed data. SEMhasnumerousapplicationswithin counseling

    psychology, as research in the field often involves testing or validating theo-

    retical models. For example, SEM is appropriate in scale development

    research to confirm the factor structure of an instrument. A researcher may

    wish to test a hypothesized factor structure of an existing instrument with a

    new population or may have established a tentative factor structure of a new

    instrument (perhaps viaexploratory factor analysis)and wish to confirm this

    factor structureon an independent sample. Counselingpsychologyresearch-

    ers arealso often interested in testing complex theoretical models in relevant

    areas (e.g., career development and multicultural development models),

    which can be accomplished effectively via SEM.


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    Perhaps because of the rapid expansion in SEM software in recent years,

    SEM is a popular technique for analyzing data in the social sciences (see

    Steiger, 2001). Unfortunately, thisexpansionin popularity coincideswiththe

    expression of many concerns in the SEM literature regarding practices of

    psychological researchers. Recent reviews of SEM research (MacCallum &

    Austin, 2000; McDonald & Ho, 2002) among various

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