Seven Steps Toward More Transparency in Statistical Practice

Autor: Casper J. Albers, Franziska A. Stanke, Štěpán Bahník, Jorge N. Tendeiro, Balazs Aczel, Alexandra Sarafoglou, David Moreau, Rink Hoekstra, Don van Ravenzwaaij, Noah van Dongen, Sil Aarts, Eric-Jan Wagenmakers, Aljaž Sluga, Johannes Algermissen
Přispěvatelé: Psychometrics and Statistics, Research and Evaluation of Educational Effectiveness, RS: CAPHRI - R1 - Ageing and Long-Term Care, Health Services Research
Rok vydání: 2021
Předmět:
MetaArXiv|Social and Behavioral Sciences|Political Science
Social Psychology
bepress|Social and Behavioral Sciences|Economics
Statistics as Topic
Acknowledgement
Behavioural sciences
Experimental and Cognitive Psychology
TABLES
GRAPHS
bepress|Social and Behavioral Sciences|Political Science
Ethos
Behavioral Neuroscience
MetaArXiv|Social and Behavioral Sciences|Other Social and Behavioral Sciences
bepress|Social and Behavioral Sciences|Social Statistics
Openness to experience
Statistical inference
Humans
Positive economics
Universalism
bepress|Social and Behavioral Sciences|Other Social and Behavioral Sciences
bepress|Social and Behavioral Sciences|Psychology
MetaArXiv|Social and Behavioral Sciences
Models
Statistical
Information Dissemination
Action
intention
and motor control
MetaArXiv|Social and Behavioral Sciences|Social Statistics
Uncertainty
Common ground
Transparency (behavior)
TRIALS
Research Design
MetaArXiv|Social and Behavioral Sciences|Economics
MetaArXiv|Social and Behavioral Sciences|Psychology
Data Interpretation
Statistical
bepress|Social and Behavioral Sciences
VISUALIZATION
Psychology
Zdroj: Nature Human Behaviour, 5, 1473-1480. Nature Publishing Group
Nature Human Behaviour, 5, 11, pp. 1473-1480
Nature human behaviour, 5(11), 1473-1480. Nature Publishing Group
ORCID
MetaArXiv
Nature Human Behaviour, 5, 1473-1480
ISSN: 2397-3374
Popis: We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.Wagenmakers and colleagues describe seven statistical procedures that increase transparency in data analysis. These procedures highlight common ground among data analysts from different schools and find inspiration in Merton's ethos of science.
Databáze: OpenAIRE
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