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Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

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  • Francis L. Huang

    (University of Missouri)

Abstract

The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.

Suggested Citation

  • Francis L. Huang, 2022. "Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 101-125, February.
  • Handle: RePEc:sae:jedbes:v:47:y:2022:i:1:p:101-125
    DOI: 10.3102/10769986211017480
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    1. Yelland Lisa N & Salter Amy B & Ryan Philip, 2011. "Relative Risk Estimation in Cluster Randomized Trials: A Comparison of Generalized Estimating Equation Methods," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-26, May.
    2. Donald Hedeker & Stephen H. C. du Toit & Hakan Demirtas & Robert D. Gibbons, 2018. "A note on marginalization of regression parameters from mixed models of binary outcomes," Biometrics, The International Biometric Society, vol. 74(1), pages 354-361, March.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    5. Højsgaard, Søren & Halekoh, Ulrich & Yan, Jun, 2005. "The R Package geepack for Generalized Estimating Equations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i02).
    6. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    7. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    8. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
    9. James E. Pustejovsky & Elizabeth Tipton, 2018. "Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 672-683, October.
    10. Maas, Cora J. M. & Hox, J.J.Joop J., 2004. "The influence of violations of assumptions on multilevel parameter estimates and their standard errors," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 427-440, June.
    11. Hin, Lin-Yee & Carey, Vincent J. & Wang, You-Gan, 2007. "Criteria for WorkingCorrelationStructure Selection in GEE: Assessment via Simulation," The American Statistician, American Statistical Association, vol. 61, pages 360-364, November.
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