Students Reading Motivation: A Multilevel Mixture Factor Analysis

  • Daniele Riggi
  • Jeroen K. Vermunt
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Latent variable modeling is a commonly used data analysis tool in social sciences and other applied fields. The most popular latent variable models are factor analysis (FA) and latent class analysis (LCA). FA assumes that there is one or more continuous latent variables – called factors – determining the responses on a set of observed variables, while LCA assumes that there is an underlying categorical latent variable – latent classes. Mixture FA is a recently proposed combination of these two models which includes both continuous and categorical latent variables. It simultaneously determines the dimensionality (factors) and the heterogeneity (latent classes) of the observed data. Both in social sciences and in biomedical field, researchers often encounter multilevel data structure. These are usually analyzed using models with random effects. Here, we present a hierarchical extension of FA called multilevel mixture factor analysis (MMFA) (Varriale and Vermunt, Multilevel mixture factor models, Under review). As in multilevel LCA (Vermunt, Sociol Methodol 33:213–239, 2003), the between-group heterogeneity is modeled by assuming that higher-level units belong to one of K latent classes. The key difference with the standard mixture FA is that the discrete mixing distribution is at the group level rather than at the individual level. We present an application of MMFA in educational research. More specifically, a FA structure is used to measure the various dimensions underlying pupils reading motivation. We assume that there are latent classes of teachers which differ in their ability of motivating children.


Latent Class Mixture Component Latent Class Analysis Finite Mixture Multivariate Normal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of Milan-BicoccaMilanItaly
  2. 2.Department of Methodology and StatisticsTilburg UniversityTilburgThe Netherlands

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