Micro–macro multilevel analysis for discrete data: A latent variable approach and an application on personal network data

M. Bennink, M.A. Croon, J.K. Vermunt

Research output: Contribution to journalArticleScientificpeer-review


A multilevel regression model is proposed in which discrete individual-level variables are used as predictors of discrete group-level outcomes. It generalizes the model proposed by Croon and van Veldhoven for analyzing micro–macro relations with continuous variables by making use of a specific type of latent class model. A first simulation study shows that this approach performs better than more traditional aggregation and disaggreagtion procedures. A second simulation study shows that the proposed latent variable approach still works well in a more complex model, but that a larger number of level-2 units is needed to retain sufficient power. The more complex model is illustrated with an empirical example in which data from a personal network are used to analyze the interaction effect of being religious and surrounding yourself with married people on the probability of being married.
Keywords: generalized linear modeling, multilevel analysis, level-2 outcome, latent class analysis, latent variable, micro–macro analysis, personal network, marriage, religion
Original languageEnglish
Pages (from-to)431-457
JournalSociological Methods and Research
Issue number4
Publication statusPublished - 2013


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