Multilevel Mixture Models

A general non-linear multilevel structural equation mixture model

In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation ...

Determining the Number of Components in Mixture Models for Hierarchical Data

Recently, various types of mixture models have been developed for data sets having a hierarchical or multilevel structure (see, e,g., [9, 12]). Most of these models include finite mixture distributions at multiple levels of a hierarchical structure. In these multilevel mixture models, selection of the number of mixture component is more complex than in standard mixture models because one has to...

Mixture models for multilevel data sets

Students Reading Motivation: A Multilevel Mixture Factor Analysis

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 cat...

Multilevel Mixture Factor Models.

Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure. These Multilevel Factor Models (MFMs) have in common that-as in multilevel regression ana...