Reporting Standards for Psychological Network Analyses in Cross-Sectional Data

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

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    74 Citations (Scopus)
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    Statistical 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.

    Original languageEnglish
    JournalPsychological Methods
    Publication statusE-pub ahead of print - 11-Apr-2022


    • Network analysis
    • Reporting standards
    • Reproducibility

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