Frank Leyva Castro
Department of Psychology
The Ohio State University
Title: Residual Diagnostics in Structural Equation Models
Abstract: Structural Equation Modeling (SEM) is a popular framework in the social sciences. As a linear model framework, it shares similar assumptions with Multiple Linear Regression (MLR) models and Multilevel Models (MLM) and Factor Analysis (FA). However, unlike these models. SEM literature on assumption assessment is surprisingly scarce, focusing mainly on outliers and influential cases. The SEM procedure allows for MLR, MLM and FA to be adjusted as special cases of SEM. We deem tenable to try to apply diagnostic approaches developed in alternative linear model literature within the scope and reach of SEM. The focus of this presentation is to showcase several approaches to residual computation and analysis, as well as to propose further development on these procedures.
Discussant: Shannon Jacoby