![]() ![]() We also tested for three predictors of p-hacking: Publication year, journal prestige, and authorship team size. Generally, results indicated that p-hacking is detectable but small in magnitude. Z-curve analyses indicated p-hacking in 11 of 18 subsets, two of which reached statistical significance. Critical bin comparisons indicated p-hacking in 12 of 18 subsets, three of which reached statistical significance. Results from two analytical approaches (i.e., z-curve, critical bin comparisons) were consistent in both direction and significance in nine of 18 datasets. We test for the prevalence and magnitude of p-hacking across the complete database as well as various subsets of the database according to common bivariate relation types in the organizational literature (e.g., attitudes-behaviors). We leverage a manually curated database of more than 1,000,000 correlation coefficients and sample sizes, with which we calculate exact p-values. We extend questionable research practices (QRPs) research by conducting a robust, large-scale analysis of p-hacking in organizational research. ![]()
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