Path Analysis With Mixed-Scale Variables: Categorical ML, Least Squares, and Bayesian Estimations.

Abstract

In applied research across education, the social and behavioral sciences, and medicine, path models frequently incorporate both continuous and ordinal manifest variables to predict binary outcomes. This study employs Monte Carlo simulations to evaluate six estimators: robust maximum likelihood with probit and logit links (MLR-probit, MLR-logit), mean- and variance-adjusted weighted and unweighted least squares (WLSMV, ULSMV), and Bayesian methods with noninformative and weakly informative priors (Bayes-NI, Bayes-WI). Across various sample sizes, variable scales, and effect sizes, results show that WLSMV and Bayes-WI consistently achieve low bias and RMSE, particularly in small samples or when mediators have few categories. By contrast, categorical MLR approaches tended to yield unstable estimates for modest effects. These findings offer practical guidance for selecting estimators in mixed-scale path analyses and underscore their implications for robust inference.