Mixture simultaneous factor analysis for capturing differences in latent variables between higher level units of multilevel data

K. De Roover, J.K. Vermunt, Marieke E. Timmerman, Eva Ceulemans

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Abstract

Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (for example, personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that may explain the covariances among variables; for example, the Big Five personality structure. In case of multilevel data, one may wonder whether or not the same covariance (factor) structure holds for each so-called ‘data block’ (containing data of one higher-level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups/blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.
Original languageEnglish
Pages (from-to)506-523
JournalStructural Equation Modeling
Volume24
Issue number4
DOIs
Publication statusPublished - 6 Mar 2017

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