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

作者：Bennink Margot^*; Croon Marcel A; Kroon Brigitte; Vermunt Jeroen K

来源：Advances in Data Analysis and Classification, 2016, 10(2): 139-154.

DOI：10.1007/s11634-016-0234-1

• Latent class analysis
• Micro-macro analysis
• Multilevel analysis
• Discrete data

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.

• 出版日期2016-6