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http://tcp.sagepub.com/The Counseling Psychologist

http://tcp.sagepub.com/content/33/3/269The 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

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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

269

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: mmartens@uamail.albany.edu.

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.

270 THE COUNSELING PSYCHOLOGIST / May 2005

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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