Advanced Applications of Structural Equation Modeling in Counseling Psychology Research

Abstract

Structural equation modeling (SEM) is a data-analytic technique that allows researchers to test complex theoretical models. Most published applications of SEM involve analyses of cross-sectional recursive (i.e., unidirectional) models, but it is possible for researchers to test more complex designs that involve variables observed at multiple points in time or variables implicated in reciprocal feedback loops (i.e., bidirectional models). Given SEM’s popularity among counseling psychology researchers, this article aims to introduce three SEM designs not often seen in the counseling psychology literature: cross-lagged panel analyses, latent growth curve modeling, and nonrecursive mediated model analysis. For each design, the authors provide a brief rationale regarding its purpose, procedures for specifying a model to test the design, and a worked illustration.