An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals
Bibliographic Data
| ID | 23387309 |
|---|---|
| Authors | Paolo Ghisletta (0000-0001-7731-2406, University of Geneva), Dario Spini (0000-0001-7171-1975, University of Lausanne) |
| Year | 2004 |
| Volume | 29 |
| Issue | 4 |
| Pages | 421-437 |
| Publication date | 2004-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Educational and Behavioral Statistics (JOURNAL) |
| Journal identifiers | ISSN: 1076-9986 • E-ISSN: 1935-1054 |
| Publisher | American Educational Research Association (AERA) (PUBLISHER) |
| DOI | 10.3102/10769986029004421 |
| OpenAlex | W2128949174 |
| Language | EN |
| Citations received | 78 |
| References cited | 53 |
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.
Data mining · Econometrics · Estimating equations · Gee · Generalized estimating equation · Longitudinal data · Longitudinal study · Maximum likelihood · Population · Regression · Regression analysis · Sociology · Statistics · Structural equation modeling · Advanced Causal Inference Techniques · Applied Mathematics · Computer Science · Demography · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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| Unique citing works | 78 |
|---|---|
| Citations per year | 3,9 |
| Citation span | 2006 - 2026 (21) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 72 |