Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes

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Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, methodology): “Some preliminary findings suggest that the BCH stepwise method might guard against biased latent class regression parameter estimates in the populations for which there is a direct effect from the covariates to the latent class indicators (Bakk & Vermunt, 2015).”
Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, methodology): “This procedure conducts Wald tests to compare the mean scores of the outcomes across groups, and was found to offer robust results even for non-normal distributed variables (Bakk & Vermunt, 2016).”
Profiles of teacher personality and relations with teacher self-efficacy, work engagement, and job satisfaction (2018, background): “The person-centered approach may be useful in not only detecting unobserved population heterogeneity in personality data but also examining the effects of trait combinations on substantively important outcomes (Bakk & Vermunt, 2016; Lanza, Tan, & Bray, 2013).”
A Comparison of Three-Step Approaches for Auxiliary Variables in Latent Class and Latent Profile Analysis (2017, background): “Auxiliary Variables The latent categorical variable can be further analyzed in LCA or LPA by investigating the association between that variable and other auxiliary variables (Bakk & Vermunt, 2016).”