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Sample Sizes and Effect Sizes are Negatively Correlated in Meta-Analyses: Evidence and Implications of a Publication Bias Against NonSignificant Findings
Abstract
Meta-analysis involves cumulating effects across studies in order to qualitatively summarize existing literatures. A recent finding suggests that the effect sizes reported in meta-analyses may be negatively correlated with study sample sizes. This prediction was tested with a sample of 51 published meta-analyses summarizing the results of 3,602 individual studies. The correlation between effect size and sample size was negative in almost 80 percent of the meta-analyses examined, and the negative correlation was not limited to a particular type of research or substantive area. This result most likely stems from a bias against publishing findings that are not statistically significant. The primary implication is that meta-analyses may systematically overestimate population effect sizes. It is recommended that researchers routinely examine the n–r scatter plot and correlation, or some other indication of publication bias and report this information in meta-analyses.
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“Given that a smaller sample size reduces statistical power-and in light of findings by Levine et al. (2009) which showed an inverse relationship between sample size and effect size in meta-analyses-this study excluded the effects of GenAI on learning attitude, knowledge retention, learning anxiety, and critical thinking.”
From Effects of Generative Artificial Intelligence on K-12 and Higher Education Students’ Learning Outcomes: A Meta-Analysis (2025) · cites this paper as Levine et al. (2009)
Citations
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“Given that the average effect size reported in meta-analyses in the social sciences are meager to begin with (Richard et al., 2003), if an upward bias exists, most effects may be even smaller that than current believed.”
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“Given the existence of publication bias has been recognized at least since the mid 1950s (Thornton & Lee, 2000; Dickersin, 2005, however traces concerns back to the late 1700s), it is not surprising that several methods of testing for publication bias have been developed.”
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Metadata
- Authors
- Levine, T. R., Asada, K. J., & Carpenter, C.
- Venue
- Communication Monographs
- Publisher
- Informa UK Limited
- Published
- 2009-08-11
- Volume
- 76
- Issue
- 3
- Pages
- 286-302
- ISSN
- 0363-7751
- 1479-5787
- Cited by
- 131
- References
- 18
- Language
- en
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