The practice of hypothesizing after results are known (HARKing) has been identified as a potential threat to the credibility of research results. We conducted simulations using input values based on comprehensive meta-analyses and reviews in applied psychology and management (e.g., strategic management studies) to determine the extent to which two forms of HARKing behaviors might plausibly bias study outcomes and to examine the determinants of the size of this effect. When HARKing involves cherry-picking, which consists of searching through data involving alternative measures or samples to find the results that offer the strongest possible support for a particular hypothesis or research question, HARKing has only a small effect on estimates of the population effect size. When HARKing involves question trolling, which consists of searching through data involving several different constructs, measures of those constructs, interventions, or relationships to find seemingly notable results worth writing about, HARKing produces substantial upward bias particularly when it is prevalent and there are many effects from which to choose. Results identify the precise circumstances under which different forms of HARKing behaviors are more or less likely to have a substantial impact on a study’s substantive conclusions and the field’s cumulative knowledge. We offer suggestions for authors, consumers of research, and reviewers and editors on how to understand, minimize, detect, and deter detrimental forms of HARKing in future research.
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When ρ is very large, ceiling effects can limit the biases produced by HARKing. When ρ is equal to or very near 0, bias is limited because the largest effect is equally likely to be negative as it is to be positive. In addition, when ρ = 0, HARKing will produce a distribution of sample effects whose mean is not changed but whose standard deviation is inflated.
Although this method is rarely encountered in the research literature, several software packages (e.g., NCSS, JMP) include an even more aggressive option—i.e., one that evaluates all possible regression models, starting with models that include two variables and examining every possible combination of predictors until the full p-variable model is tested.
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Appendix. R Codes Used in Simulation Studies
Appendix. R Codes Used in Simulation Studies
The codes below calculate the expected results if 100% of studies engage in either cherry-picking or question trolling. The final estimates of the values expected if some proportion of all studies involve either cherry-picking or question trolling are obtained by calculating the weighted average (weighted by estimated prevalence) of the values produced by the codes below and the expected value of R = 0.20 if there is no cherry-picking or question trolling.
R code for cherry-picking
R code for question trolling
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Murphy, K.R., Aguinis, H. HARKing: How Badly Can Cherry-Picking and Question Trolling Produce Bias in Published Results?. J Bus Psychol 34, 1–17 (2019). https://doi.org/10.1007/s10869-017-9524-7
- Publication bias
- Data snooping