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An Assessment of Estimation Procedures for Multilevel Models with Binary Responses


  • Germáan Rodríguez
  • Noreen Goldman


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.

Suggested Citation

  • Germáan Rodríguez & Noreen Goldman, 1995. "An Assessment of Estimation Procedures for Multilevel Models with Binary Responses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 73-89, January.
  • Handle: RePEc:bla:jorssa:v:158:y:1995:i:1:p:73-89
    DOI: 10.2307/2983404

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    Cited by:

    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    2. Dana A. Glei & Noreen Goldman & German Rodriguez, 2002. "Utilization of Care During Pregnancy in Rural Guatemala: Does Obstetrical Need Matters," Working Papers 308, Princeton University, Woodrow Wilson School of Public and International Affairs, Office of Population Research..
    3. David Cutts & Edward Fieldhouse, 2009. "What Small Spatial Scales Are Relevant as Electoral Contexts for Individual Voters? The Importance of the Household on Turnout at the 2001 General Election," American Journal of Political Science, John Wiley & Sons, vol. 53(3), pages 726-739, July.
    4. Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    5. Adeniyi, Isaac Adeola & Yahya, Waheed Babatunde, 2020. "Bayesian Generalized Linear Mixed Effects Models Using Normal-Independent Distributions: Formulation and Applications," MPRA Paper 99165, University Library of Munich, Germany.
    6. Ana Maria Fernandez-Pujals & Mark James Adams & Pippa Thomson & Andrew G McKechanie & Douglas H R Blackwood & Blair H Smith & Anna F Dominiczak & Andrew D Morris & Keith Matthews & Archie Campbell & P, 2015. "Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS)," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-18, November.
    7. Jerry J. Maples & Susan A. Murphy & William G. Axinn, 2002. "Two-Level Proportional Hazards Models," Biometrics, The International Biometric Society, vol. 58(4), pages 754-763, December.
    8. Daniel J Corsi & S V Subramanian & Martin McKee & Wei Li & Sumathi Swaminathan & Patricio Lopez-Jaramillo & Alvaro Avezum & Scott A Lear & Gilles Dagenais & Sumathy Rangarajan & Koon Teo & Salim Yusuf, 2012. "Environmental Profile of a Community’s Health (EPOCH): An Ecometric Assessment of Measures of the Community Environment Based on Individual Perception," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.

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