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Evaluating Quantitative Methods For Intercategorical-Intersectionality Research: A Simulation Study, Mayuri Mahendran

Electronic Thesis and Dissertation Repository

This study evaluated eight quantitative methods for their predictive accuracy for intersectionally-defined subgroups, via a simulation study. The methods included two forms of single-level regression with interaction terms, cross-classification, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), and four decision tree methods: classification and regression trees (CART), conditional inference trees, chi-square automatic interaction detector, and random forest. The simulated datasets varied by outcome variable type, input variable types, sample size, and size and direction of the effects. Predictive accuracy improved with increasing sample size for all methods except CART. At small sample sizes, random forest and MAIHDA generally created …