The Untrustworthy Evidence in Dishonesty Research

Abstract

Replicable and reliable research is essential for cumulative science and its applications in practice. This article examines the quality of research on dishonesty using a sample of 278 hand-coded test statistics from 99 articles. Z-curve analysis indicates a low expected replication rate, a high proportion of missing studies, and an inflated false discovery risk. Test of insufficient variance (TIVA) finds that 11/61 articles with multiple test statistics contain results that are `too-good-to-be-true'. Sensitivity analysis confirms the robustness of the findings. In conclusion, caution is advised when relying on or applying the existing literature on dishonesty.