用广义估计方程分析截面聚类数据

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2021-06-04 DOI:10.3102/10769986211017480

Francis L. Huang

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引用次数: 12

摘要

聚类数据的存在在社会行为科学中很常见。一种专门处理聚类数据但在教育中很少使用的方法是广义估计方程（GEEs）方法。我们提供了GEE的背景，讨论了为什么它适合分析聚类数据，并提供了使用连续结果和二元结果的实例。对GEE、多级模型和普通最小二乘法结果进行了比较，以突出两种方法之间的异同。使用R和SPSS Version 26提供了详细的演练。

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Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

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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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.