Reviewing SPSS Amos for Analyzing Categorical Data in Structural Equation Modeling: A Bayesian Approach

• In Event: Division D: Multiple Linear Regression: The General Linear Model SIG

Abstract

This study aims to help applied investigators and practitioners to fully understand the roles of categorical data in structural equation modeling (SEM) and the appropriate strategies of analyzing such data under the popular SPSS AMOS software. To that end, the study elaborates on several cases where different types of categorical variables are incorporated into SEM to be either exogenous or endogenous data. After that, the study focuses on using Bayesian statistics to analyze categorical data and presents two demonstration examples. During the demonstrations, the study introduces the appropriate language for reporting Bayesian estimation results and provides additional references. The study concludes with significance and utility of the presented strategies for its intended audience.

Authors

• Hongwei Yang, University of West Florida

• Lihua Xu, Western Carolina University

• Xing Liu, Eastern Connecticut State University

• Hyungsoo Kim

• Jing Yuan