Result: Statistical evidence in psychological networks.
Title:
Statistical evidence in psychological networks.
Authors:
Huth KBS; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. k.huth@uva.nl.; Department of Psychiatry, Amsterdam UMC-University of Amsterdam, Amsterdam, The Netherlands. k.huth@uva.nl.; Centre for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands. k.huth@uva.nl.; Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands. k.huth@uva.nl., Haslbeck JMB; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.; Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands., Keetelaar S; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands., van Holst RJ; Department of Psychiatry, Amsterdam UMC-University of Amsterdam, Amsterdam, The Netherlands.; Centre for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands., Marsman M; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
Source:
Nature human behaviour [Nat Hum Behav] 2025 Oct 09. Date of Electronic Publication: 2025 Oct 09.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Publishing Country of Publication: England NLM ID: 101697750 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2397-3374 (Electronic) Linking ISSN: 23973374 NLM ISO Abbreviation: Nat Hum Behav Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : Springer Nature Publishing, [2017]-
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Entry Date(s):
Date Created: 20251009 Latest Revision: 20251009
Update Code:
20251010
DOI:
10.1038/s41562-025-02314-2
PMID:
41068490
Database:
MEDLINE
Further information
Psychometric network models have become increasingly popular in psychology and the social sciences as tools to explore multivariate data. In these models, constructs are represented as networks of observed variables, and researchers often interpret the presence or absence of edges as evidence for or against conditional associations between variables. However, the statistical evidence supporting these edges is rarely evaluated. Here we show that a large proportion of reported network findings is based on weak or inconclusive evidence. We reanalysed 293 networks from 126 published papers using a Bayesian approach that quantifies the evidence for each edge. Across the studies, one-third of edges showed inconclusive evidence (1/3 < inclusion Bayes factor (BF <subscript>10</subscript> ) < 3), about half showed weak evidence (BF <subscript>10</subscript> > 3 or BF <subscript>10</subscript> < 1/3) and fewer than 20% were strongly supported (BF <subscript>10</subscript> > 10 or BF <subscript>10</subscript> < 1/10). Networks based on relatively large sample sizes yielded more-robust results. Our study shows that networks are often supported by too little evidence from the data for the results to be reported with confidence, not meaning that the results are flawed but, rather, suggesting caution in interpreting individual edges.
(© 2025. The Author(s), under exclusive licence to Springer Nature Limited.)
Competing interests: The authors declare no competing interests.