The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multilevel Logistic Regression for Polytomous Data and Rankings
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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Parametric empirical Bayes inference: theory and applications
Morris, C. N. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477920 · Cited by 1,448 · cited 2×
“We suggest using parametric empirical Bayes methods (e.g., Morris, 1983) for predicting values of the latent variables for the individual units.”
MethodPlackett, R. L. (1975) · Applied Statistics · doi:10.2307/2346567 · Cited by 731
Cited by
Self‐Priming in Production: Evidence for a Hybrid Model of Syntactic Priming
Jacobs, C. L., Cho, S., & Watson, D. G. (2019) · Cognitive Science · doi:10.1111/cogs.12749 · Cited by 24
Metadata
- Authors
- Skrondal, A., & Rabe-Hesketh, S.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- 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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