Journal of Business and Psychology

, Volume 34, Issue 1, pp 1–17 | Cite as

HARKing: How Badly Can Cherry-Picking and Question Trolling Produce Bias in Published Results?

  • Kevin R. MurphyEmail author
  • Herman Aguinis
Original Paper


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.


HARKing Simulation Publication bias Data snooping 


  1. Aguinis, H., & Vandenberg, R. J. (2014). An ounce of prevention is worth a pound of cure: Improving research quality before data collection. Annual Review of Organizational Psychology and Organizational Behavior, 1, 569–595.Google Scholar
  2. Aguinis, H., Werner, S., Abbott, J. L., Angert, C., Park, J. H., & Kohlhausen, D. (2010). Customer-centric science: Reporting significant research results with rigor, relevance, and practical impact in mind. Organizational Research Methods, 13, 515–539.Google Scholar
  3. Aguinis, H., Dalton, D. R., Bosco, F. A., Pierce, C. A., & Dalton, C. M. (2011). Meta-analytic choices and judgment calls: Implications for theory building and testing, obtained effect sizes, and scholarly impact. Journal of Management, 37, 5–38.Google Scholar
  4. Aguinis, H., Shapiro, D. L., Antonacopoulou, E., & Cummings, T. G. (2014). Scholarly impact: A pluralist conceptualization. Academy of Management Learning and Education, 13, 623–639.Google Scholar
  5. Aguinis, H., Cascio, W. F., & Ramani, R. S. (2017). Science’s reproducibility and replicability crisis: International business is not immune. Journal of International Business Studies, 48, 653–663.Google Scholar
  6. Aguinis, H., Ramani, R. S., & Alabduljader, N. (in press). What you see is what you get? Enhancing methodological transparency in management research. Academy of Management Annals.
  7. Bamberger, P., & Ang, S. (2016). The quantitative discovery: What is it and how to get it published. Academy of Management Discoveries, 2, 1–6.Google Scholar
  8. Banks, G. C., O’Boyle, E. H., Pollack, J. M., White, C. D., Batchelor, J. H., Whelpley, C. E., …, Adkins, C. L. (2016a). Questions about questionable research practices in the field of management: A guest commentary. Journal of Management, 42, 5–20.Google Scholar
  9. Banks, G. C., Rogelberg, S. G., Woznyj, H. M., Landis, R. S., & Rupp, D. E. (2016b). Editorial: Evidence on questionable research practices: The good, the bad and the ugly. Journal of Business and Psychology, 31, 323–338.Google Scholar
  10. Bedeian, A. G., Taylor, S. G., & Miller, A. N. (2010). Management science on the credibility bubble: Cardinal sins and various misdemeanors. Academy of Management Learning & Education, 9, 715–725.Google Scholar
  11. Bergh, D. D., Aguinis, H., Heavey, C., Ketchen, D. J., Boyd, B. K., Su, P., Lau, C., & Joo, H. (2016). Using meta-analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership-performance relationship. Strategic Management Journal, 37, 477–497.Google Scholar
  12. Bergh, D. D., Sharp, B. M., Aguinis, H., & Li, M. (2017). Is there a credibility crisis in strategic management research? Evidence on the reproducibility of study findings. Strategic Organization, 15, 423–436.Google Scholar
  13. Bernerth, J., & Aguinis, H. (2016). A critical review and best-practice recommendations for control variable usage. Personnel Psychology, 69, 229–283.Google Scholar
  14. Bettis, R. A., Ethiraj, S., Gambardella, A., Helfat, C., & Mitchell, W. (2016). Creating repeatable cumulative knowledge in strategic management: A call for a broad and deep conversation among authors, referees, and editors. Strategic Management Journal, 37, 257–261.Google Scholar
  15. Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley.Google Scholar
  16. Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100, 431–449.Google Scholar
  17. Bosco, F. A., Aguinis, H., Field, J. G., Pierce, C. A., & Dalton, D. R. (2016). HARKing’s threat to organizational research: Evidence from primary and meta-analytic sources. Personnel Psychology, 69, 709–750.Google Scholar
  18. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum.Google Scholar
  19. Cortina, J. M., & Landis, R. S. (2009). When small effect sizes tell a big story, and when large effect sizes don’t. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity, and fable in the organizational and social sciences (pp. 287–308). New York: Routledge.Google Scholar
  20. Cortina, J. M., Aguinis, H., & DeShon, R. P. (2017). Twilight of dawn or of evening? A century of research methods in the Journal of Applied Psychology. Journal of Applied Psychology, 102, 274–290.Google Scholar
  21. Derksen, S., & Keselman, H. J. (1992). Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology, 45, 265–282.Google Scholar
  22. Edwards, J. R., Berry JW. (2010). The presence of something or the absence of nothing: Increasing theoretical precision in management research. Organizational Research Methods, 13, 668–689.
  23. Fanelli, D. (2009). How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLoS One, 4, e5738.Google Scholar
  24. Fisher, G., & Aguinis, H. (2017). Using theory elaboration to make theoretical advancements. Organizational Research Methods, 20, 438–464.Google Scholar
  25. Grand, J. A., Rogelberg, S. G., Allen, T. D., Landis, R. S., Reynolds, D. H., Scott, J. C., Tonidandel, S., & Truxillo, D. M. (in press). A systems-based approach to fostering robust science in industrial-organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice. Google Scholar
  26. Hambrick DC. (2007). The field of management’s devotion to theory: Too much of a good thing? Academy of Management Journal, 50, 1346–1352.
  27. Harrell, H. (2011). Regression modeling strategies with applications to linear models, logistic regression and survival analysis. New York: Springer-Verlag.Google Scholar
  28. Hayduk, L. A. (1987). Structural equation modeling with LISREL: Essentials and advances. Baltimore: Johns Hopkins University Press.Google Scholar
  29. Hitchcock, C., & Sober, E. (2004). Prediction versus accommodation and the risk of overfitting. British Journal for the Philosophy of Science, 55, 1–34.Google Scholar
  30. Hollenbeck, J. H., & Wright, P. M. (2017). Harking, sharking, and tharking: Making the case for post hoc analysis of scientific data. Journal of Management, 43, 5–18.Google Scholar
  31. Honig, B., Lampel, J., Siegel, D., & Drnevich, P. (2014). Ethics in the production and dissemination of management research: Institutional failure or individual fallibility. Journal of Management Studies, 51, 118–142.Google Scholar
  32. Hubbard R, Armstrong JS. (1997). Publication bias against null results. Psychological Reports, 80, 337–338.
  33. Jensen, A. (1980). Bias in mental testing. New York: Free Press.Google Scholar
  34. John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth-telling. Psychological Science, 23, 524–532.Google Scholar
  35. Judd, C. M., & McClelland, G. H. (1989). Data analysis: A model comparison approach. New York: Harcourt.Google Scholar
  36. Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality & Social Psychology Review, 2, 196.Google Scholar
  37. Ketchen, D. J., Boyd, B. K., & Bergh, D. D. (2008). Research methodology in strategic management past accomplishments and future challenges. Organizational Research Methods, 11, 643–658.Google Scholar
  38. Ketchen, D. J., Ireland, R. D., & Baker, L. T. (2013). The use of archival proxies in strategic management studies: Castles made of sand? Organizational Research Methods, 16, 32–42.Google Scholar
  39. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press.Google Scholar
  40. Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological Methods, 21, 475–492.Google Scholar
  41. Landis, R. S., Edwards, B. D., & Cortina, J. M. (2009). On the practice of allowing correlated residuals among indicators in structural equation models. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences (pp. 193–214). New York: Routledge/Taylor & Francis Group.Google Scholar
  42. Leung, K. (2011). Presenting post hoc hypotheses as a priori: Ethical and theoretical issues. Management and Organization Review, 7, 471–479.Google Scholar
  43. Lipton, P. (2005). Testing hypotheses: Prediction and prejudice. Science, 307, 219–221.Google Scholar
  44. Lo, A. W., & MacKinlay, A. C. (1990). Data-snooping biases in tests of financial asset pricing models. Review of Financial Studies, 3, 431–467.Google Scholar
  45. Locke, E. A. (2007). The case for inductive theory building. Journal of Management, 33, 867–890.Google Scholar
  46. Locke, K., Golden-Biddle, K., & Feldman, M. S. (2008). Perspective-making doubt generative: Rethinking the role of doubt in the research process. Organization Science, 19, 907–918.Google Scholar
  47. Murphy, K. R., & Cleveland, J. N. (1995). Understanding performance appraisal: Social, organizational and goal-oriented perspectives. Newbury Park: Sage.Google Scholar
  48. Neuroskeptic. (2012). The nine circles of scientific hell. Perspectives on Psychological Science, 7, 643–644.Google Scholar
  49. O’Boyle, E. H., Banks, G. C., & Gonzalez-Mulé, E. (2017). The chrysalis effect: How ugly initial results metamorphosize into beautiful articles. Journal of Management, 43, NPi.
  50. Orlitzky M. (2012). How can significance tests be deinstitutionalized? Organizational Research Methods, 15, 199–228.
  51. Pfeffer J. (2007). A modest proposal: How we might change the process and prod- uct of managerial research. Academy of Management Journal, 50, 1334–1345.
  52. Pigliucci, M. (2009). The end of theory in science? EMBO Reports, 10, 534.Google Scholar
  53. Shaw, J. B. (2017). Advantages of starting with theory. Academy of Management Journal, 60, 819–822.Google Scholar
  54. Shen, W., Kiger, T. B., Davies, S. E., Rasch, R. L., Simon, K. M., & Ones, D. S. (2011). Samples in applied psychology: Over a decade of research in review. Journal of Applied Psychology, 96, 1055–1064.Google Scholar
  55. Sörbom, D. (1989). Model modification. Psychometrika, 54, 371–384.Google Scholar
  56. Thurstone, L. L. (1934). The vectors of the mind. American Psychologist, 41, 1–32.Google Scholar
  57. Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.). (2016). Big data at work: The data science revolution and organizational psychology. New York: Routledge.Google Scholar
  58. Wasserman, R. (2013). Ethical issues and guidelines for conducting data analysis in psychological research. Ethics and Behavior, 23, 3–15.Google Scholar
  59. White R. (2003). The epistemic advantage of prediction over accommodation. Mind, 112, 653–683.  https://doi.10.1093/mind/112.448.653
  60. Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594–604.Google Scholar
  61. Wing, H. (1982). Statistical hazards in the determination of adverse impact with small samples. Personnel Psychology, 35, 153–162.Google Scholar
  62. Wright, P. M. (2016). Ensuring research integrity: An editor’s perspective. Journal of Management, 42, 1037–1043.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Personnel and Employment Relations, Kemmy Business SchoolUniversity of LimerickLimerickIreland
  2. 2.George Washington UniversityWashingtonUSA

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