Abstract
We consider an explorative method for unsupervised clustering of upper level units in a two-level hierarchical setting. The idea lies in applying a density-based clustering algorithm to the predicted random effects obtained from a multilevel cumulative logit model. We illustrate the proposed approach throughout the analysis of data from European Social Survey about political trust in European countries.
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Details on HDI are at http://essedunet.nsd.uib.no/cms/topics/multilevel/ch6/all.html.
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Grilli, L., Panzera, A., Rampichini, C. (2018). Clustering Upper Level Units in Multilevel Models for Ordinal Data. In: Mola, F., Conversano, C., Vichi, M. (eds) Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55708-3_15
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