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横断面数据中心理网络分析的报告标准。

Reporting standards for psychological network analyses in cross-sectional data.

机构信息

Amsterdam Centre for Urban Mental Health, University of Amsterdam.

Department of Psychology, University of Amsterdam.

出版信息

Psychol Methods. 2023 Aug;28(4):806-824. doi: 10.1037/met0000471. Epub 2022 Apr 11.

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 of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be 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).

摘要

描述心理数据中多元依赖结构的统计网络模型越来越受欢迎。这些相对新颖的统计技术需要特定的指南,以便让研究界能够理解和使用。到目前为止,研究人员已经提供了教程,指导网络及其准确性的研究。然而,目前在确定分析和结果的哪些部分应该在科学报告中报告方面,指导很少。缺乏这样的报告标准可能会增加研究人员的自由度,并为有问题的报告做法提供肥沃的土壤。在这里,我们介绍了横截面数据中网络分析的报告标准,以及一个教程和两个示例。提出的指南旨在面向研究人员以及更广泛的科学界,例如评估科学工作的审稿人和期刊编辑。最后,我们讨论了网络文献如何特别受益于此类报告和透明度的指南。

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