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Meta-analytic interval estimation for bivariate correlations
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DOI：10.1037/a0012868.png)
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摘要
En 中文
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.
Keyword:
confidence interval
fixed effects
random effects
research synthesis
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