Scientific Misconduct in Psychology

Stricker, Johannes; Günther, Armin

doi:10.1027/2151-2604/a000356

Cited by 21 publications

(9 citation statements)

References 46 publications

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“…Rubbo (2017) found that majority of retractions (~ 65%) were issued by Editors in the field of engineering. Stricker and Günther ( 2019 ) found that 0.82 papers were retracted per 10,000 journal articles in the field of psychology due to scientific misconduct. According to Wang et al ( 2019 ), longest duration has taken for retraction due to fraud/suspected fraud while shortest duration for authorship disputes.…”

Section: Previous Studiesmentioning

confidence: 99%

Retracted articles in the biomedical literature from Indian authors

Elango¹

2021

Scientometrics

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The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

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Section: Previous Studiesmentioning

confidence: 99%

Retracted articles in the biomedical literature from Indian authors

Elango¹

2021

Scientometrics

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“…Thus, plain language summaries facilitate access to research outputs for the general public, which has been discussed as a promising avenue for sustaining trust in science (Grand et al, 2012;Pittinsky, 2015). We argue that sustaining trust in science is especially relevant in (social) psychology-a discipline which investigates topics of high relevance to the public, but which also struggles with a replicability crisis (Klein et al, 2018;Open Science Collaboration, 2015;Świątkowski & Dompnier, 2017) and has been stricken by several misconduct cases lately (Callaway, 2011;Stricker & Günther, 2019;Świątkowski & Dompnier, 2017). Without this trust, scientific findings are likely at risk of being marginalized, which may even lead to a proliferation of conspiracy theories.…”

Section: Introduction Introductionmentioning

confidence: 97%

Straight From the Scientist’s Mouth—Plain Language Summaries Promote Laypeople’s Comprehension and Knowledge Acquisition When Reading About Individual Research Findings in Psychology

Kerwer

Chasiotis

Stricker

et al. 2021

Collabra: Psychology

Self Cite

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Easily comprehensible summaries of scholarly articles that are provided alongside ‘ordinary’ scientific abstracts, so-called plain language summaries, can be a powerful tool for communicating research findings to a wider audience. Using an experimental within-person-design in a preregistered study (N = 166), we showed that the comprehensibility for laypeople was higher for plain language summaries compared to scientific abstracts in a psychological journal and also found that laypeople actually understood the corresponding information more correctly for plain language summaries. Moreover, in line with the easiness effect of science popularization, individuals perceived plain language summaries as more credible and were more confident about their ability to make a decision based on plain language summaries. If and under which circumstances this higher perceived credibility is justified, is discussed together with other practical implications and theoretical implications of our findings. In sum, our research further strengthens arguments for providing plain language summaries of psychological research findings by demonstrating that they actually work in practice.

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“…After all, even if we could classify all fabricated data correctly and falsely regard genuine data as fabricated in 5% of the cases, then with a prevalence of 2% (Fanelli, 2009) the positive predictive value would only be 29%. This is a best-case scenario (see also Stricker & Günther, 2019) that would cause approximately 1 out of 3 cases of 'detected data fabrication' to be false. Hence, we do not recommend attempting to detect data fabrication on statistical methods alone.…”

Section: Discussionmentioning

confidence: 99%

Detection of data fabrication using statistical tools

Hartgerink¹,

Voelkel²,

Wicherts³

et al. 2019

Preprint

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Scientific misconduct potentially invalidates findings in many scientific fields. Improved detection of unethical practices like data fabrication is considered to deter such practices. In two studies, we investigated the diagnostic performance of various statistical methods to detect fabricated quantitative data from psychological research. In Study 1, we tested the validity of statistical methods to detect fabricated data at the study level using summary statistics. Using (arguably) genuine data from the Many Labs 1 project on the anchoring effect (k=36) and fabricated data for the same effect by our participants (k=39), we tested the validity of our newly proposed 'reversed Fisher method', variance analyses, and extreme effect sizes, and a combination of these three indicators using the original Fisher method. Results indicate that the variance analyses perform fairly well when the homogeneity of population variances is accounted for and that extreme effect sizes perform similarly well in distinguishing genuine from fabricated data. The performance of the 'reversed Fisher method' was poor and depended on the types of tests included. In Study 2, we tested the validity of statistical methods to detect fabricated data using raw data. Using (arguably) genuine data from the Many Labs 3 project on the classic Stroop task (k=21) and fabricated data for the same effect by our participants (k=28), we investigated the performance of digit analyses, variance analyses, multivariate associations, and extreme effect sizes, and a combination of these four methods using the original Fisher method. Results indicate that variance analyses, extreme effect sizes, and multivariate associations perform fairly well to excellent in detecting fabricated data using raw data, while digit analyses perform at chance levels. The two studies provide mixed results on how the use of random number generators affects the detection of data fabrication. Ultimately, we consider the variance analyses, effect sizes, and multivariate associations valuable tools to detect potential data anomalies in empirical (summary or raw) data. However, we argue against widespread (possible automatic) application of these tools, because some fabricated data may be irregular in one aspect but not in another. Considering how violations of the assumptions of fabrication detection methods may yield high false positive or false negative probabilities, we recommend comparing potentially fabricated data to genuine data on the same topic.

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Scientific Misconduct in Psychology

Cited by 21 publications

References 46 publications

Retracted articles in the biomedical literature from Indian authors

Retracted articles in the biomedical literature from Indian authors

Straight From the Scientist’s Mouth—Plain Language Summaries Promote Laypeople’s Comprehension and Knowledge Acquisition When Reading About Individual Research Findings in Psychology

Detection of data fabrication using statistical tools

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