Analyzing the Effect of Latent Variable Models on Structural Equation Model Accuracy and Interpretability

Posted: Sep 24, 2016

• Samuel Smith

Abstract

This research addresses a critical gap in the methodological literature by systematically examining how different latent variable modeling approaches influence both the statistical accuracy and theoretical interpretability of structural equation models. The study investigates the integration of advanced latent variable modeling techniques from machine learning and deep learning domains to address challenges in traditional SEM frameworks when confronted with modern data characteristics including high dimensionality, complex measurement structures, and non-normal distributions.