Addressing Publication Bias in Meta-Analysis Empirical Findings From Community-Augmented Meta-Analyses of Infant Language Development

Meta-analyses are an indispensable research synthesis tool for characterizing bodies of literature and advancing theories. One important open question concerns the inclusion of unpublished data into meta-analyses. Finding such studies can be effortful, but their exclusion potentially leads to consequential biases like overestimation of a literature's mean effect. We address two questions about unpublished data using MetaLab, a collection of community-augmented meta-analyses focused on developmental psychology. First, we assess to what extent MetaLab datasets include gray literature, and by what search strategies they are unearthed. We find that an average of 11% of datapoints are from unpublished literature; standard search strategies like database searches, complemented with individualized approaches like including authors' own data, contribute the majority of this literature. Second, we analyze the effect of including versus excluding unpublished literature on estimates of effect size and publication bias, and find this decision does not affect outcomes. We discuss lessons learned and implications.