心理信息
人气
计算机科学
透明度(行为)
数据科学
依赖关系(UML)
心理学
梅德林
人工智能
社会心理学
计算机安全
政治学
法学
作者
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
出处
期刊:Psychological Methods
[American Psychological Association]
日期:2022-04-11
卷期号:28 (4): 806-824
被引量:219
摘要
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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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