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An Assessment of Estimation Procedures for Multilevel Models with Binary Responses
Abstract
We evaluate two software packages that are available for fitting multilevel models to binary response data, namely VARCL and ML3, by using a Monte Carlo study designed to represent quite closely the actual structure of a data set used in an analysis of health care utilization in Guatemala. We find that the estimates of fixed effects and variance components produced by the software packages are subject to very substantial downward bias when the random effects are sufficiently large to be interesting. In fact, the fixed effect estimates are no better than the estimates obtained by using standard logit models that ignore the hierarchical structure of the data. The estimates of standard errors appear to be reasonably accurate and superior to those obtained by ignoring clustering, although one might question their utility in the presence of large biases. We conclude that alternative estimation procedures need to be developed and implemented for the binary response case
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Cites
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Harville, D. A. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480998 · Cited by 2,667
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A comparison of Bayesian and likelihood-based methods for fitting multilevel models
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Multilevel Modeling: When and Why
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Jewkes, R. K., Levin, J. B., & Penn-Kekana, L. A. (2003) · Social Science & Medicine · doi:10.1016/s0277-9536(02)00012-6 · Cited by 395
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Classification of common human diseases derived from shared genetic and environmental determinants
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Revisiting Robinson: The perils of individualistic and ecologic fallacy
Subramanian, S. V., Jones, K., Kaddour, A., & Krieger, N. (2009) · International Journal of Epidemiology · doi:10.1093/ije/dyn359 · Cited by 252
Austin, P. C. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1195 · Cited by 193
Structural bias in the sentencing of felony defendants
Sutton, J. R. (2013) · Social Science Research · doi:10.1016/j.ssresearch.2013.04.003 · Cited by 142
Bayesian Multilevel Analysis and MCMC
Draper, D. (2007) · Handbook of Multilevel Analysis · doi:10.1007/978-0-387-73186-5_2 · Cited by 69
Optimal Designs for Multilevel Studies
Moerbeek, M., Breukelen, G. J. P. V., & Berger, M. P. F. (2007) · Handbook of Multilevel Analysis · doi:10.1007/978-0-387-73186-5_4 · Cited by 28
Metadata
- Authors
- Rodriguez, G., & Goldman, N.
- Venue
- Journal of the Royal Statistical Society. Series A (Statistics in Society)
- Publisher
- Oxford University Press (OUP)
- Published
- 1995
- Volume
- 158
- Issue
- 1
- Pages
- 73
- ISSN
- 0964-1998 (ISSN-L)
- OpenAlex
- W2029974256
- Cited by
- 476
- References
- 1
- FWCI
- 17.35
- Type
- journal article
- Language
- en
- Open access
- closed
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