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.

中文翻译：

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横截面数据中心理网络分析的报告标准。

描述心理数据中多变量依赖结构的统计网络模型越来越受欢迎。这种相当新颖的统计技术需要特定的指导方针，以使研究界能够使用它们。到目前为止，研究人员已经提供了指导网络估计及其准确性的教程。然而，目前在确定应记录分析和结果的哪些部分方面几乎没有指导在一份科学报告中。缺乏此类报告标准可能会提高研究人员的自由度，并可能为可疑的报告实践提供肥沃的土壤。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。所提出的指南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具体从此类报告和透明度指南中受益。