Structural Equation Modeling: Principles, Assumptions, and Practical Application Using SPSS Amos (Published)
Structural Equation Modeling (SEM) is a comprehensive multivariate statistical technique that permits the testing of complex theoretical models involving observed and latent variables. This article reviews the foundational principles and assumptions of SEM and outlines the practical steps for conducting SEM analysis using SPSS AMOS. As well, it presents implications to research and practice utilizing SEM, as well as directions for future research with the technique, with particular emphasis on social science fields. An example using a real data set is used to demonstrate how to construct a model, interpret path coefficients, evaluate model fit, and ensure that SEM assumptions are met. While the data used here are real, the emphasis remains on understanding the SEM framework and enhancing methodological rigor in research applications
Keywords: SPSS AMOS, Structural equation modeling, latent variables, model assumptions, model fit