Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data. Issue 4 (4th July 2017)

• Record Type: Journal Article
• Authors: De Roover, Kim; Vermunt, Jeroen K.; Timmerman, Marieke E.; Ceulemans, Eva
• Is Part Of: Structural equation modeling. Volume 24:Issue 4(2017)
• Page Start: 506
• Page End: 523
• Publication Date: 2017-07-04
• Subjects: factor analysis -- latent variables -- mixture model clustering -- multilevel data; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1278604
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (e.g., personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that might explain the covariances among variables; for example, the Big Five personality structure. In the case of multilevel data, one might wonder whether or not the same covariance (factor) structure holds for each so-called data block (containing data of 1 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 or 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.