Structural Equation Modeling: Reviewing the Basics and Moving Forward

TLDR

This tutorial provides an overview of structural equation modeling (SEM), outlining its purpose, goals, and key terminology, and notes that the concepts illustrated also apply to personality scale analysis. The tutorial focuses on confirmatory factor analysis (CFA), distinguishing it from exploratory factor analysis (EFA) and outlining the advantages of CFA techniques. The tutorial demonstrates the SEM modeling process with a practical example using HIV risk behavior data, covering analysis of nonnormally distributed data, model modification strategies, and the structure of drug and alcohol use problem scales, and provides corresponding EQS 6.1 syntax and output.

Abstract

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.