Details

Autor(en) / Beteiligte

• Burger, Julian
• Isvoranu, Adela-Maria
• Lunansky, Gabriela
• Haslbeck, Jonas M. B.
• Epskamp, Sacha
• Hoekstra, Ria H. A.
• Fried, Eiko I.
• Borsboom, Denny
• Blanken, Tessa F.
• Steinley, Douglas

Titel

Reporting Standards for Psychological Network Analyses in Cross-Sectional Data

Ist Teil von

• Psychological methods, 2023-08, Vol.28 (4), p.806-824

Ort / Verlag

United States: American Psychological Association

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EBSCOhost APA PsycARTICLES

Beschreibungen/Notizen

• 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. Translational Abstract In recent years, network models have become increasingly popular in the field of psychology. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding how network analysis can be applied to psychological data. 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 result in researchers being confronted with too much choice in reporting their results, which in turn might 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.