Micro–macro multilevel analysis for discrete data: A latent variable approach and an application on personal network data
Bennink,M. Croon,M.A. Vermunt,J.K.
Bennink,M.
Croon,M.A.
Vermunt,J.K.
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. Keywords: generalized linear modeling, multilevel analysis, level-2 outcome, latent class analysis, latent variable, micro–macro analysis, personal network, marriage, religion
Description
Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Bennink, M, Croon, M A & Vermunt, J K 2013, 'Micro–macro multilevel analysis for discrete data : A latent variable approach and an application on personal network data', Sociological Methods and Research, vol. 42, no. 4, pp. 431-457. https://doi.org/10.1177/0049124113500479