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Differentiating Classes From Dimensions Under Unfavorable Data Conditions: Monte Carlo Comparisons Of Taxometric And Latent Variable Mixture Models, Anthony Olufemi Ahmed

Dissertations

Taxometric and latent variable mixture models can aid in (1) determining whether the source of population heterogeneity, a latent variable, θ, is best explain by a dimensional (one-class) or taxonic (two-class) model and (2) distinguishing between constructs continuously distributed and those that are Bernoulli distributed at the latent level. Although these models have gained widespread use in psychology research, few have been systematically evaluated to determine the robustness of their results when statistical assumptions are violated (e.g., severe skew, unequal mixing proportions, etc.). The current study examined the performance of Meehl’s taxometric procedures and two latent variable mixture models: a …