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Gauss or Bernoulli?

Author

Listed:
  • Peter J. Hannan

    (University of Minnesota)

  • David M. Murray

    (University of Minnesota)

Abstract

This Monte Carlo study compares performance of the linear and the logistic mixed-model analyses of simulated community trials having event rates of 37%, 13%, or 5%, intraclass correlations between 0.01 and 0.05, and 17 or 5 denominator degrees of freedom. Type I or Type II error rates showed no essential difference between the two analysis methods. They showed depressed error rates when the event rate or the denominator degrees of freedom were small. The authors conclude that in studies with adequate denominator degrees of freedom, the researcher may use either method of analysis but should accept negative estimates of components of variance to avoid depression of error rates.

Suggested Citation

  • Peter J. Hannan & David M. Murray, 1996. "Gauss or Bernoulli?," Evaluation Review, , vol. 20(3), pages 338-352, June.
    • Handle: RePEc:sae:evarev:v:20:y:1996:i:3:p:338-352
    DOI: 10.1177/0193841X9602000306
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    References listed on IDEAS

    as
    1. D. A. Williams, 1982. "Extra‐Binomial Variation in Logistic Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 144-148, June.
    2. 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.
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