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In Defense of HARKing

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  • Vancouver, Jeffrey B.

Abstract

Science is a complex task. It involves the creation and dissemination of knowledge. The creation of knowledge requires identifying and abstracting patterns (i.e., identifying phenomena and theorizing about the processes that bring it about), as well as systematically observing to better see and quantify the patterns (e.g., effect size estimating) or assess the validity of the abstractions used to explain the patterns (i.e., theory testing). To help (a) hone in on what observations would be useful and (b) communicate what the patterns mean, we are encouraged to develop and report hypotheses. That is, strategically, hypotheses facilitate the planning of data collection by helping the researcher understand what patterns need to be observed to assess the merit of an explanation. Meanwhile, tactically, hypotheses help focus the audience on the crucial patterns needed to answer a question or test a theory. When the strategic hypotheses are not supported, it raises a question regarding what to do tactically. Depending on the result (i.e., different direction; null), one might construct a hypothesis to facilitate dissemination without reporting this post hoc construction or remove mention of a hypothesis altogether. This practice is called HARKing (i.e., hypothesizing after results are known). HARKing has been so disparaged as to be considered a “detrimental research practice” (Grand et al., 2018, p. 6). As such, the Society for Industrial and Organizational Psychology's (SIOP) Robust and Reliability Science task force appears to be recommending that HARKing not be taught by educators, encouraged by reviewers or editors, or practiced by authors. I do not agree with those recommendations, and I elaborate on my position below.

Suggested Citation

  • Vancouver, Jeffrey B., 2018. "In Defense of HARKing," Industrial and Organizational Psychology, Cambridge University Press, vol. 11(1), pages 73-80, March.
  • Handle: RePEc:cup:inorps:v:11:y:2018:i:01:p:73-80_00
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    Cited by:

    1. Rubin, Mark, 2020. "Does preregistration improve the credibility of research findings?," MetaArXiv vgr89, Center for Open Science.
    2. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    3. Denis G. Arnold, 2021. "Universal research ethics and international business studies," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(7), pages 1229-1237, September.
    4. Mattia Prosperi & Jiang Bian & Iain E. Buchan & James S. Koopman & Matthew Sperrin & Mo Wang, 2019. "Raiders of the lost HARK: a reproducible inference framework for big data science," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-12, December.

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