Structural Equation Modeling: Reviewing the Basics and Moving Forward

Jodie B. Ullman

861 citations0/0 files storedJournal of Personality Assessment

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Psychometric Methodologies and Testing Personality Traits and Psychology Mental Health Research Topics

Abstract

This tutorial begins with an overview of structural equation modeling (SEM) that includes the purpose and goals of the statistical analysis as well as terminology unique to this technique. I will focus on confirmatory factor analysis (CFA), a special type of SEM. After a general introduction, CFA is differentiated from exploratory factor analysis (EFA), and the advantages of CFA techniques are discussed. Following a brief overview, the process of modeling will be discussed and illustrated with an example using data from a HIV risk behavior evaluation of homeless adults (Stein & Nyamathi, 2000). Techniques for analysis of nonnormally distributed data as well as strategies for model modification are shown. The empirical example examines the structure of drug and alcohol use problem scales. Although these scales are not specific personality constructs, the concepts illustrated in this article directly correspond to those found when analyzing personality scales and inventories. Computer program syntax and output for the empirical example from a popular SEM program (EQS 6.1; Bentler, 2001) are included.

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References

1999Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives 1992Alternative Ways of Assessing Model Fit 1990Comparative fit indexes in structural models.1989Structural Equations with Latent Variables 1991Structural Equations with Latent Variables.1998Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.1996Power analysis and determination of sample size for covariance structure modeling.1989EQS : structural equations program manual

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