Abstract
The currently available meta-analytic methods for correlations have restrictive assumptions. The fixed-effects methods assume equal population correlations and exhibit poor performance under correlation heterogeneity. The random-effects methods do not assume correlation homogeneity but are based on an equally unrealistic assumption that the selected studies are a random sample from a well-defined superpopulation of study populations. The random-effects methods can accommodate correlation heterogeneity, but these methods do not perform properly in typical applications where the studies are nonrandomly selected. A new fixed-effects meta-analytic confidence interval for bivariate correlations is proposed that is easy to compute and performs well under correlation heterogeneity and nonrandomly selected studies.
Keywords
Bivariate analysisStatisticsCorrelationMathematicsConfidence intervalHomogeneity (statistics)Random effects modelEconometricsMeta-analysisSample size determinationPopulation
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Publication Info
- Year
- 2008
- Type
- article
- Volume
- 13
- Issue
- 3
- Pages
- 173-181
- Citations
- 96
- Access
- Closed
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Cite This
Douglas G. Bonett
(2008).
Meta-analytic interval estimation for bivariate correlations..
Psychological Methods
, 13
(3)
, 173-181.
https://doi.org/10.1037/a0012868
Identifiers
- DOI
- 10.1037/a0012868