The intergroup contact-prejudice link survives file-drawer checks, indicating limited publication bias.

Evidence

According to our uncorrected effect size estimate for all samples based on the random effects model (see Table 1), it would require more than 1,200 missing samples that average no effect to erase the significance of the intergroup contact and prejudice association at the 5% level of confidence. This figure is considerably larger than the 713 samples uncovered by our intensive 6-year search. Next, we check on publication bias by determining that the relationship between sample sizes and effect sizes is not significant for either the original set of 713 samples, r .02, p .67, or the 696 samples included in our analysis, r .04, p .33. Large samples provide more reliable results, and this lack of a relationship between sample size and effect size is a crude indicator of limited publication bias. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 104

Categorization

• DEFINE: Clarifies what the fail-safe N and the sample-size/effect-size correlation check are and how they signal publication bias.
• XREF: connects to: Meta-analyses overestimate effect sizes because null results go unpublished. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 44; The fail-safe index underestimates publication bias by assuming missing studies have no effect. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 46; Publication bias can be detected through sample-size and effect-size correlations. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 47 (+1 more)

Related

• The fail-safe index underestimates publication bias by assuming missing studies have no effect. (similarity 0.89)
• Comparing published and unpublished studies tests whether reporting bias inflates effects. (similarity 0.88)
• Publication bias can be detected through sample-size and effect-size correlations. (similarity 0.87)
• The contact-prejudice effect may be partly an artifact of publication bias. (similarity 0.87)
• Meta-analyses overestimate effect sizes because null results go unpublished. (similarity 0.87)
• Contact’s prejudice-reducing effect is causal, not a selection or publication artifact. (similarity 0.85)
• A null correlation between sample size and effect size indicates minimal publication bias. (similarity 0.83)
• Stronger contact effects in rigorous studies would confirm the contact-prejudice link. (similarity 0.83)
• More rigorous contact studies show larger prejudice-reduction effects. (similarity 0.82)
• No meta-analysis of intergroup contact research existed before this study. (similarity 0.82)
• Participant selection bias does not inflate contact-prejudice effect sizes. (similarity 0.81)
• Randomized contact experiments yield near-large prejudice reduction effects. (similarity 0.80)
• Meta-analysis pools all studies to estimate effect sizes and moderators (similarity 0.80)
• Contact studies with cleaner control groups show larger effects. (similarity 0.80)
• The trim-and-fill adjustment increases the contact-prejudice effect estimate, supporting its robustness. (similarity 0.80)