Dominance Analysis for Latent Variable Models: A Comparison of Methods With Categorical Indicators and Misspecified Models

Abstract

Dominance analysis (DA) is a very useful tool for ordering independent variables in a regression model based on their relative importance in explaining variance in the dependent variable. This approach, which was originally described by Budescu, has recently been extended to use with structural equation models examining relationships among latent variables. Research demonstrated that this approach yields accurate results for latent variable models involving normally distributed indicator variables and correctly specified models. The purpose of the current simulation study was to compare the use of this DA approach to a method based on observed regression DA and DA when the latent variable model is estimated using two-stage least squares for latent variable models with categorical indicators and/or model misspecification. Results indicated that the DA approach for latent variable models can provide accurate ordering of the variables and correct hypothesis selection when indicators are categorical and models are misspecified. A discussion of implications from this study is provided.