Fitting marginal models in small samples: A simulation study of marginalized multilevel models and generalized estimating equations

2021 ◽  
Author(s):  
Ruofan Bie ◽  
Sebastien Haneuse ◽  
Nathan Huey ◽  
Jonathan Schildcrout ◽  
Glen McGee
Keyword(s):  
Simulation Study ◽  
Generalized Estimating Equations ◽  
Multilevel Models ◽  
Estimating Equations ◽  
Small Samples ◽  
Marginal Models ◽  
Generalized Estimating

Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

2021 ◽  
pp. 107699862110174
Author(s):  
Francis L. Huang
Keyword(s):  
Least Squares ◽  
Generalized Estimating Equations ◽  
Multilevel Models ◽  
Clustered Data ◽  
Estimating Equations ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Binary Outcomes ◽  
Similarities And Differences ◽  
Generalized Estimating

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.

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