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    Research practices and assessment of research misconduct

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        This article discusses the responsible conduct of research, questionable research practices, and research misconduct. Responsible conduct of research is often defined in terms of a set of abstract, normative principles, professional standards, and ethics in doing research. In order to accommodate the normative principles of scientific research, the professional standards, and a researcher’s moral principles, transparent research practices can serve as a framework for responsible conduct of research. We suggest a “prune-and-add” project structure to enhance transparency and, by extension, responsible conduct of research. Questionable research practices are defined as practices that are detrimental to the research process. The prevalence of questionable research practices remains largely unknown, and reproducibility of findings has been shown to be problematic. Questionable practices are discouraged by transparent practices because practices that arise from them will become more apparent to scientific peers. Most effective might be preregistrations of research design, hypotheses, and analyses, which reduce particularism of results by providing an a priori research scheme. Research misconduct has been defined as fabrication, falsification, and plagiarism (FFP), which is clearly the worst type of research practice. Despite it being clearly wrong, it can be approached from a scientific and legal perspective. The legal perspective sees research misconduct as a form of white-collar crime. The scientific perspective seeks to answer the following question: “Were results invalidated because of the misconduct?” We review how misconduct is typically detected, how its detection can be improved, and how prevalent it might be. Institutions could facilitate detection of data fabrication and falsification by implementing data auditing. Nonetheless, the effect of misconduct is pervasive: many retracted articles are still cited after the retraction has been issued.

        Main points
        1. Researchers systematically evaluate their own conduct as more responsible than colleagues, but not as responsible as they would like.

        2. Transparent practices, facilitated by the Open Science Framework, help embody scientific norms that promote responsible conduct.

        3. Questionable research practices harm the research process and work counter to the generally accepted scientific norms, but are hard to detect.

        4. Research misconduct requires active scrutiny of the research community because editors and peer-reviewers do not pay adequate attention to detecting this. Tips are given on how to improve your detection of potential problems.

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        Most cited references 79

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        This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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            False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant.

            In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.

              Author and article information

              Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
              Author notes
              [* ]Corresponding author’s e-mail address: c.h.j.hartgerink@
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              ScienceOpen Research
              02 August 2016
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              © 2016 Hartgerink and Wicherts

              This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at .

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