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Sökning: WFRF:(Wagenmakers M.) > Stockholms universitet

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  • Aczel, Balazs, et al. (författare)
  • Consensus-based guidance for conducting and reporting multi-analyst studies
  • 2021
  • Ingår i: eLIFE. - : eLife Sciences Publications. - 2050-084X. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.
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3.
  • Silberzahn, Raphael, et al. (författare)
  • Many analysts, one dataset : Making transparent how variations in analytical choices affect results
  • 2018
  • Ingår i: Advances in Methods and Practices in Psychological Science. - : Sage Publications. - 2515-2459 .- 2515-2467. ; 1:3, s. 337-356
  • Tidskriftsartikel (refereegranskat)abstract
    • Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 differentanalyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results.
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