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Get Free AccessStatistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Julian Burger, Adela‐Maria Isvoranu, Gabriela Lunansky, Jonas M B Haslbeck, Sacha Epskamp, Ria H. A. Hoekstra, Eiko I. Fried, Denny Borsboom, Tessa F. Blanken (2022). Reporting standards for psychological network analyses in cross-sectional data.. Psychological Methods, 28(4), pp. 806-824, DOI: 10.1037/met0000471.
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Type
Article
Year
2022
Authors
9
Datasets
0
Total Files
0
Language
English
Journal
Psychological Methods
DOI
10.1037/met0000471
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