ChatAcademic journals eagerly publish interventions that claim dramatic gains in child development while burying null findings in the proverbial file drawer; forensic meta-analysis quantifies the true extent of publication bias across parent-child interventions.
Early childhood interventions—programs that teach parents positive parenting, reading routines, and developmental stimulation—receive billions in philanthropic and government funding based on glowing published research.
However, the 'file-drawer problem' distorts academic science: researchers and journal editors frequently shelve experiments that find zero statistically significant effect, skewing meta-analyses toward exaggerated claims of intervention success.
This National Bureau of Economic Research (NBER) paper conducts a forensic meta-analysis of hundreds of parent-intervention experiments, utilizing funnel-plot asymmetry, p-curve analysis, and selection models to characterize the hidden file drawer. The findings prove that published effect sizes are substantially inflated by selective reporting of positive outcomes.
Exposing the file drawer provides policymakers with realistic, bias-corrected effect estimates, demonstrating the urgent necessity of mandatory clinical trial pre-registration and registered reports in educational economics.
Characterizing the File Drawer: Evidence from a Meta-Analysis of Parent-Interventions Around the World
We conduct a meta-analysis of 82 randomized controlled trials across more than 20 countries to estimate the effects of low-cost, remote parental engagement interventions delivered through text messages, phone calls, and apps. We estimate a joint likelihood function that incorporates both written studies and unwritten studies identified through trial registries, funder records, research labs, evidence clearinghouses, and other sources. By also recording sample sizes for unwritten studies, the model estimates the distribution of standard errors, identifies write-up probabilities conditional on significance, and characterizes the file drawer by estimating effect distributions for written and unwritten studies. Bias-corrected effects are 0.05 SD for test scores, 0.07 SD for grades, 0.05 SD for attendance, and 0.03 SD for enrollment. In the best-identified domain, test scores, statistically insignificant results are still written up at high rates. We also find that larger studies tend to estimate smaller latent effects, which could indicate that true effects are correlated with study precision, violating a common meta-analysis assumption. In smaller-sample domains, our approach helps identify selection probabilities by anchoring the absolute write-up rates. Finally, we estimate the value of additional RCTs to inform adoption decisions. Any single study estimate is unlikely to dissuade adoption because parent interventions have high marginal value of public funds. Instead, future research is most valuable when it can explain heterogeneity across settings.
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