Meta-Analytic Methods to Detect Publication Bias in Behavior Science Research

Publication bias is an issue of great concern across a range of scientific fields. Although less documented in the behavior science fields, there is a need to explore viable methods for evaluating publication bias, in particular for studies based on single-case experimental design logic. Although publication bias is often detected by examining differences between meta-analytic effect sizes for published and grey studies, difficulties identifying the extent of grey studies within a particular research corpus present several challenges. We describe in this article several meta-analytic techniques for examining publication bias when published and grey literature are available as well as alternative meta-analytic techniques when grey literature is inaccessible. Although the majority of these methods have primarily been applied to meta-analyses of group design studies, our aim is to provide preliminary guidance for behavior scientists who might use or adapt these techniques for evaluating publication bias. We provide sample data sets and R scripts to follow along with the statistical analysis in hope that an increased understanding of publication bias and respective techniques will help researchers understand the extent to which it is a problem in behavior science research.

Automated summary

Publication bias is a significant concern in the scientific community, and exploring methods to evaluate and address it is crucial. This article discusses meta-analytic techniques for examining publication bias in behavior science research, aiming to provide guidance for researchers in this field. The provided sample data sets and R scripts serve as a practical tool for behavior scientists to better understand and combat publication bias.