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Reporting Standards for Psychological Network Analyses in Cross-Sectional Data.

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Title: Reporting Standards for Psychological Network Analyses in Cross-Sectional Data.
Authors: Burger, Julian1,2 j.burger@uva.nl, Isvoranu, Adela-Maria3, Lunansky, Gabriela3, Haslbeck, Jonas M. B.3, Epskamp, Sacha1,3, Hoekstra, Ria H. A.3, Fried, Eiko I.4, Borsboom, Denny3, Blanken, Tessa F.3
Source: Psychological Methods. Aug2023, Vol. 28 Issue 4, p806-824. 19p.
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Methods is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reporting Standards for Psychological Network Analyses in Cross-Sectional Data.
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Methods%22">Psychological Methods</searchLink>. Aug2023, Vol. 28 Issue 4, p806-824. 19p.
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Methods is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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