Abstract
When the process of publication favors studies with small p-values, and hence large effect estimates, combined estimates from many studies may be biased. This paper describes a model for estimation of effect size when there is selection based on one-tailed p-values. The model employs the method of maximum likelihood in the context of a mixed (fixed and random) effects general linear model for effect sizes. It offers a test for the presence of publication bias, and corrected estimates of the parameters of the linear model for effect magnitude. The model is illustrated using a well-known data set on the benefits of psychotherapy.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 419-435 |
| Number of pages | 17 |
| Journal | Psychometrika |
| Volume | 60 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 1995 |
Keywords
- effect size
- meta-analysis
- mixed models
- publication bias
- research synthesis
- selection models
ASJC Scopus subject areas
- Psychology(all)
- Applied Mathematics
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Cite this
Vevea, J. L., & Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60(3), 419-435. https://doi.org/10.1007/BF02294384