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Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models

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  • Amy E. Wagler

    (The University of Texas at El Paso)

Abstract

Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for estimating the heterogeneity due to clustering on a scale that is easy to interpret. The performance of the proposed asymptotic intervals and percentile bootstrap intervals are compared by simulations and in an application.

Suggested Citation

  • Amy E. Wagler, 2014. "Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models," Journal of Educational and Behavioral Statistics, , vol. 39(3), pages 167-179, June.
    • Handle: RePEc:sae:jedbes:v:39:y:2014:i:3:p:167-179
    DOI: 10.3102/1076998614529159
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    References listed on IDEAS

    as
    1. Schenker N. & Gentleman J. F., 2001. "On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals," The American Statistician, American Statistical Association, vol. 55, pages 182-186, August.
    2. Klaus Larsen & Jørgen Holm Petersen & Esben Budtz-Jørgensen & Lars Endahl, 2000. "Interpreting Parameters in the Logistic Regression Model with Random Effects," Biometrics, The International Biometric Society, vol. 56(3), pages 909-914, September.
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    Cited by:

    1. Cristiano C. Santos & Rosangela H. Loschi, 2017. "Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 209-230, March.

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