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Determinants of selective reporting: A taxonomy based on content analysis of a random selection of the literature

Author

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  • Jenny T van der Steen
  • Cornelis A van den Bogert
  • Mirjam C van Soest-Poortvliet
  • Soulmaz Fazeli Farsani
  • René H J Otten
  • Gerben ter Riet
  • Lex M Bouter

Abstract

Background: Selective reporting is wasteful, leads to bias in the published record and harms the credibility of science. Studies on potential determinants of selective reporting currently lack a shared taxonomy and a causal framework. Objective: To develop a taxonomy of determinants of selective reporting in science. Design: Inductive qualitative content analysis of a random selection of the pertinent literature including empirical research and theoretical reflections. Methods: Using search terms for bias and selection combined with terms for reporting and publication, we systematically searched the PubMed, Embase, PsycINFO and Web of Science databases up to January 8, 2015. Of the 918 articles identified, we screened a 25 percent random selection. From eligible articles, we extracted phrases that mentioned putative or possible determinants of selective reporting, which we used to create meaningful categories. We stopped when no new categories emerged in the most recently analyzed articles (saturation). Results: Saturation was reached after analyzing 64 articles. We identified 497 putative determinants, of which 145 (29%) were supported by empirical findings. The determinants represented 12 categories (leaving 3% unspecified): focus on preferred findings (36%), poor or overly flexible research design (22%), high-risk area and its development (8%), dependence upon sponsors (8%), prejudice (7%), lack of resources including time (3%), doubts about reporting being worth the effort (3%), limitations in reporting and editorial practices (3%), academic publication system hurdles (3%), unfavorable geographical and regulatory environment (2%), relationship and collaboration issues (2%), and potential harm (0.4%). Conclusions: We designed a taxonomy of putative determinants of selective reporting consisting of 12 categories. The taxonomy may help develop theory about causes of selection bias and guide policies to prevent selective reporting.

Suggested Citation

  • Jenny T van der Steen & Cornelis A van den Bogert & Mirjam C van Soest-Poortvliet & Soulmaz Fazeli Farsani & René H J Otten & Gerben ter Riet & Lex M Bouter, 2018. "Determinants of selective reporting: A taxonomy based on content analysis of a random selection of the literature," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
    • Handle: RePEc:plo:pone00:0188247
    DOI: 10.1371/journal.pone.0188247
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Emily S Sena & H Bart van der Worp & Philip M W Bath & David W Howells & Malcolm R Macleod, 2010. "Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy," PLOS Biology, Public Library of Science, vol. 8(3), pages 1-8, March.
    3. Mojtaba Vaismoradi & Hannele Turunen & Terese Bondas, 2013. "Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study," Nursing & Health Sciences, John Wiley & Sons, vol. 15(3), pages 398-405, September.
    4. Daniele Fanelli, 2010. "Do Pressures to Publish Increase Scientists' Bias? An Empirical Support from US States Data," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-7, April.
    5. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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    1. Marjan Bakker & Coosje L S Veldkamp & Marcel A L M van Assen & Elise A V Crompvoets & How Hwee Ong & Brian A Nosek & Courtney K Soderberg & David Mellor & Jelte M Wicherts, 2020. "Ensuring the quality and specificity of preregistrations," PLOS Biology, Public Library of Science, vol. 18(12), pages 1-18, December.
    2. Horbach, Serge & Aagaard, Kaare & Schneider, Jesper W., 2021. "Meta-Research: How problematic citing practices distort science," MetaArXiv aqyhg, Center for Open Science.

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