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Cited by 186

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Multilevel Logistic Regression for Polytomous Data and Rankings

Skrondal, Anders, Rabe-Hesketh, Sophia·2003·Psychometrika

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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Metadata

Authors
Skrondal, A., & Rabe-Hesketh, S.
Venue
Psychometrika
Published
2003-06
Volume
68
Issue
2
Pages
267-287
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
OpenAlex
W2072983788
Cited by
186
References
2
FWCI
9.13
Type
journal article
Language
en
Open access
closed

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