Structural Equation Modeling and Path Analysis: Foundational Concepts and Practical Applications

Imagine you want to untangle a web of relationships, like figuring out whether study habits directly boost exam scores or if that effect happens indirectly through reduced anxiety. Traditional statistics might leave you squinting at a tangle of separate regressions, but structural equation modeling (SEM) hands you a single, powerful lens. Structural equation modeling (SEM) is a multivariate statistical technique used to examine relationships among observed and latent (unobserved) variables. Unlike traditional regression, SEM allows researchers to test theoretical models by evaluating both measurement models (how latent variables relate to their observed indicators) and structural models (how variables influence one another). It accounts for measurement error and can assess direct, indirect, and total effects within a single analytical framework. SEM encompasses three primary models: the path analysis model, the confirmatory factor analysis model, and the structural regression model. This chapter focuses specifically on the path analysis model. As a subset of SEM, path analysis is concerned exclusively with analyzing relationships among observed variables. It enables researchers to test hypotheses about mediation and direct effects, making it particularly well-suited for research in areas such as education, psychology, business, and health sciences.