Abstract
We propose a unifying framework for multilevel modeling of polytomous data and rankings, accommodating dependence induced by factor and/or random coefficient structures at different levels. The framework subsumes a wide range of models proposed in disparate methodological literatures. Partial and tied rankings, alternative specific explanatory variables and alternative sets varying across units are handled. The problem of identification is addressed. We develop an estimation and prediction methodology for the model framework which is implemented in the generally available gllamm software. The methodology is applied to party choice and rankings from the 1987–1992 panel of the British Election Study. Three levels are considered: elections, voters and constituencies.
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Parts of this work were completed while Anders Skrondal visited the Biostatistics Group at The University of Manchester, UK. gllamm and the script for the analyses in this article can be downloaded from: http://www.iop.kcl.ac.uk/IoP/Departments/BioComp/programs/gllamm.html.
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Skrondal, A., Rabe-Hesketh, S. Multilevel logistic regression for polytomous data and rankings. Psychometrika 68, 267–287 (2003). https://doi.org/10.1007/BF02294801
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DOI: https://doi.org/10.1007/BF02294801