An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals
Open accessAbstract
Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.
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Generalized estimating equations assume correlated dependent-variable observations and are fairly robust to missing data and covariance-matrix misspecification.
“We used generalized estimating equations (GEE), which is an extension of the general linear model (GLM) that assumes correlated observations of dependent variables and are fairly robust to missing data and to misspecifications of the covariance matrix (e.g., Ghisletta & Spini, 2004; Liang & Zeger, 1986).”
From Adaptive and maladaptive emotion regulation strategies: Interactive effects during CBT for social anxiety disorder (2014) · cites this paper as Ghisletta & Spini, 2004
The study used a generalized estimating equation (GEE) to account for correlation between multiple observations nested within counties.
“For our main analysis, we employed a generalized estimating equation (GEE) to account for the correlation between multiple observations in the dataset based on county.”
From Transfer of Minors to Adult Court in California: Investigating the Legal/Demographic Factors that Make the Difference (2025) · cites this paper as Ghisletta & Spini, 2004
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References 72 · Cited by 388
- Authors
- Ghisletta, P., & Spini, D.
- Affiliations
- University of Geneva (CH)
- University of Lausanne (CH)
- Venue
- Journal of Educational and Behavioral Statistics
- Published
- 2004-12
- Volume
- 29
- Issue
- 4
- Pages
- 421-437
- ISSN
- 1076-9986 (ISSN-L)
- 1935-1054
- OpenAlex
- W2128949174
- Cited by
- 388
- References
- 72
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 24 · 2023: 34 · 2022: 21 · 2021: 24 · 2020: 20 · 2019: 21 · 2018: 26 · 2017: 25 · 2016: 25 · 2015: 36 · 2014: 29 · 2013: 25 · 2012: 8
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of Educational and Behavioral Statistics (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- generalized estimating equations, correlated data, clustered data, longitudinal data, oldest old
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