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Micro–Macro Multilevel Analysis for Discrete Data

Author

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  • Margot Bennink
  • Marcel A. Croon
  • Jeroen K. Vermunt

Abstract

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.

Suggested Citation

  • Margot Bennink & Marcel A. Croon & Jeroen K. Vermunt, 2013. "Micro–Macro Multilevel Analysis for Discrete Data," Sociological Methods & Research, , vol. 42(4), pages 431-457, November.
    • Handle: RePEc:sae:somere:v:42:y:2013:i:4:p:431-457
    DOI: 10.1177/0049124113500479
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    References listed on IDEAS

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    1. Jean-Paul Fox & Cees Glas, 2003. "Bayesian modeling of measurement error in predictor variables using item response theory," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 169-191, June.
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