Micro–macro multilevel latent class models with multiple discrete individual-level variables

Margot Bennink (), Marcel A. Croon (), Brigitte Kroon () and Jeroen K. Vermunt ()
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Margot Bennink: Tilburg University
Marcel A. Croon: Tilburg University
Brigitte Kroon: Tilburg University
Jeroen K. Vermunt: Tilburg University

Advances in Data Analysis and Classification, 2016, vol. 10, issue 2, No 2, 139-154

Abstract: Abstract An existing micro–macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the individual-level data are summarized at the group-level by constructing a discrete latent variable at the group level and this group-level latent variable is used as a predictor for the group-level outcome. In the first extension, that is referred to as the Direct model, the multiple individual-level variables are directly used as indicators for the group-level latent variable. In the second extension, referred to as the Indirect model, the multiple individual-level variables are used to construct an individual-level latent variable that is used as an indicator for the group-level latent variable. This implies that the individual-level variables are used indirectly at the group-level. The within- and between components of the (co)varn the individual-level variables are independent in the Direct model, but dependent in the Indirect model. Both models are discussed and illustrated with an empirical data example.

Keywords: Latent class analysis; Micro-macro analysis; Multilevel analysis; Discrete data (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11634-016-0234-1

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