Scientists' Perceptions of Organizational Justice and Self-Reported Misbehaviors

Martinson, Brian C.; Anderson, Melissa S.; Crain, A. Lauren; Vries, Raymond De

doi:10.1525/jer.2006.1.1.51

Cited by 161 publications

(139 citation statements)

References 60 publications

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“…This may lead established scientists to believe that early-career scientists are more likely to engage in QRPs (and thus fit the storybook image less well) than PhD students and established scientists, but studies comparing self-admitted usage of QRPs and misbehavior between scientists of different career-stages have yielded mixed results. Some studies found that younger scientists are more likely to admit to undesirable scientific behavior Tijdink et al, 2014), while other studies found that older scientists are more likely to admit to this kind of behavior (Martinson, Anderson, Crain, & De Vries, 2006;Martinson, Anderson, & de Vries, 2005). Another explanation might be sought in the idea that Ph.D. students represent potential rather than practice, making it easier to imagine them as matching the ideal.…”

Section: Discussionmentioning

confidence: 99%

Doctoral thesis: The human fallibility of scientists - dealing with error and bias in academic research

Veldkamp¹

2017

Preprint

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THE HUMAN FALLIBILITY OF SCIENTISTSDealing with error and bias in academic researchRecent studies have highlighted that not all published findings in the scientific literature are trustworthy, suggesting that currently implemented control mechanisms such as high standards for the reporting of research methods and results, peer review, and replication, are not sufficient. In psychology in particular, solutions are sought to deal with poor reproducibility and replicability of research results. In this dissertation project I considered these problems from the perspective that the scien¬tific enterprise must better recognize the human fallibility of scientists, and I examined potential solutions aimed at dealing with human error and bias in psychological science. First, I studied whether the human fallibility of scientists is actually recognized (Chapter 2). I examined the degree to which scientists and lay people believe in the storybook image of the scientist: the image that scientists are more objective, rational, open-minded, intelligent, honest and communal than other human beings. The results suggested that belief in this storybook image is strong, particularly among scientists themselves. In addition, I found indications that scientists believe that scientists like themselves fit the storybook image better than other scientists. I consider scientist’s lack of acknowledgement of their own fallibility problematic, because I believe that critical self-reflection is the first line of defense against potential human error aggravated by confirmation bias, hindsight bias, motivated reasoning, and other human cognitive biases that could affect any professional in their work. Then I zoomed in on psychological science and focused on human error in the use of null the most widely used statistical framework in psychology: hypothesis significance testing (NHST). In Chapters 3 and 4, I examined the prevalence of errors in the reporting of statistical results in published articles, and evaluated a potential best practice to reduce such errors: the so called ‘co-pilot model of statistical analysis’. This model entails a simple code of conduct prescribing that statistical analyses are always conducted independently by at least two persons (typically co-authors). Using statcheck, a software package that is able to quickly retrieve and check statistical results in large sets of published articles, I replicated the alarmingly high error rates found in earlier studies. Although I did not find support for the effectiveness of the co-pilot model in reducing these errors, I proposed several ways to deal with human error in (psychological) research and suggested how the effectiveness of the proposed practices might be studied in future research. Finally, I turned to the risk of bias in psychological science. Psychological data can often be analyzed in many different ways. The often arbitrary choices that researchers face in analyzing their data are called researcher degrees of freedom. Researchers might be tempted to use these researcher degrees of freedom in an opportunistic manner in their pursuit of statistical significance (often called p-hacking). This is problematic because it renders research results unreliable. In Chapter 5 I presented a list of researcher degrees of freedom in psychological studies, focusing on the use of NHST. This list can be used to assess the potential for bias in psychological studies, it can be used in research methods education, and it can be used to examine the effectiveness of a potential solution to restrict oppor¬tunistic use of RDFs: study pre-registration. Pre-registration requires researchers to stipulate in advance the research hypothesis, data collection plan, data analyses, and what will be reported in the paper. Different forms of pre-registration are currently emerging in psychology, mainly varying in terms of the level of detail with respect to the research plan they require researchers to provide. In Chapter 6, I assessed the extent to which current pre-registrations restricted opportunistic use of the researcher degrees of freedom on the list presented in Chapter 5. We found that most pre-registrations were not sufficiently restrictive, but that those that were written following better guidelines and requirements restricted opportunistic use of researcher degrees of freedom considerably better than basic pre-registrations that were written following a limited set of guidelines and requirements. We concluded that better instructions, specific questions, and stricter requirements are necessary in order for pre-registrations to do what they are supposed to do: to protect researchers from their own biases.

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Section: Discussionmentioning

confidence: 99%

Doctoral thesis: The human fallibility of scientists - dealing with error and bias in academic research

Veldkamp¹

2017

Preprint

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“…One way of creating an environment that is less conducive of research misconduct is to be sensitive to the needs of the researchers and their sense of organizational justice. In the survey by Martinson et al, selfreported scientific misconduct by scientists correlated positively with their perceptions of distributive and procedural injustice (Martinson et al 2006). Another survey by Keith-Spiegel et al showed that researchers expect fairness and respect from the Institutional Review Boards (IRBs) (Keith-Spiegel et al, 2006).…”

Section: "Systems Approach" To Primary Preventionmentioning

confidence: 99%

A Theoretical Comparison of the Models of Prevention of Research Misconduct

Kumar¹

2010

Accountability in Research

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The current methods of dealing with research misconduct involve detection and rectification after the incident has already occurred. This method of monitoring scientific integrity exerts considerable negative effects on the concerned persons and is also wasteful of time and resources. Time has arrived for research administrators to focus seriously on prevention of misconduct. In this article, preventive models suggested earlier by Weed and Reason have been combined to arrive at six models of prevention. This is an effort to streamline the thinking regarding misconduct prevention, so that the advantages and disadvantages of each can be weighed and the method most appropriate for the institute chosen.

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“…Two more studies analysed effects of experienced nonreciprocity at work on intentions to leave one's job prematurely and observed strong, consistent associations (Hasselhorn et al, 2004;Siegrist et al, 2007). The question of whether the frustration of work-related rewards increases the risk of norm-breaking behaviour has been studied in the context of a large anonymous survey of several thousand early-career and mid-career researchers in the United States (Martinson et al, 2006). It was found that a high effort-reward imbalance ratio was related to an increased probability of scientific misbehaviour in early-career, but not in mid-career scientists.…”

Section: Brief Summary Of Empirical Evidence On Health-adverse Effectsmentioning

confidence: 99%

Unfair exchange and health: Social bases of stress-related diseases

Siegrist

2009

Soc Theory Health

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In this contribution it is argued that the analysis of exchange characteristics of core social roles in adult life offers a promising approach towards studying the social bases of stress-related diseases. More specifically, the theoretical model of effort-reward imbalance claims that the violation of a core principle of social exchange, reciprocity, elicits sustained stress reactions with adverse long-term effects on health. This model has been extensively tested with regard to the work role, and a brief review of empirical evidence is given, based on findings from prospective epidemiological studies. Taken together, results demonstrate that the risk of incident stress-related disease, such as coronary heart disease or depression, is about twice as high in men and women scoring high on effort-reward imbalance at work compared to non-exposed people. Finally, recent extensions of the theoretical model beyond the work role and beyond health as the main study outcome are discussed, illustrating the utility of this concept for general sociology.

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Scientists' Perceptions of Organizational Justice and Self-Reported Misbehaviors

Cited by 161 publications

References 60 publications

Doctoral thesis: The human fallibility of scientists - dealing with error and bias in academic research

Doctoral thesis: The human fallibility of scientists - dealing with error and bias in academic research

A Theoretical Comparison of the Models of Prevention of Research Misconduct

Unfair exchange and health: Social bases of stress-related diseases

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