Class Enumeration in Mixture Modeling with Nested Data: A Brief Report

Abstract

Given the increased focus of educational research on what works for whom and under what circumstances over the last decade, educational researchers are increasingly turning toward mixture models to identify heterogeneous subgroups among students. Such data are inherently nested, as students are nested within classrooms and schools. Yet there has been limited guidance on which specifications are most appropriate for enumerating latent classes when data are nested. This study utilized longitudinal, state-collected student data to demonstrate the impact of different specifications (i.e., ignoring nested data, using a post-hoc adjustment, and a parametric and non-parametric approach) of a latent class model when analyzing nested data. The overarching goal of this study was to provide the implications of four different model specifications commonly used to adjust for clustering in the context of mixture modeling. We highlight factors that may influence researchers’ decisions to employ one approach over another when conducting multilevel mixture modeling. We conclude with a set of recommendations that may be particularly helpful for the use of these methods in educational settings, where nested data is common.