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Auditing the research practices and statistical analyses of the group-level temporal network approach to psychological constructs: A systematic scoping review

数据科学 领域(数学) 计算机科学 多样性(控制论) 审计 一致性(知识库) 人工智能 数学 经济 管理 纯数学
作者
M. Annelise Blanchard,Alba Contreras,Rana Begum Kalkan,Alexandre Heeren
出处
期刊:Behavior Research Methods [Springer Science+Business Media]
卷期号:55 (2): 767-787 被引量:37
标识
DOI:10.3758/s13428-022-01839-y
摘要

Network analyses have become increasingly common within the field of psychology, and temporal network analyses in particular are quickly gaining traction, with many of the initial articles earning substantial interest. However, substantial heterogeneity exists within the study designs and methodology, rendering it difficult to form a comprehensive view of its application in psychology research. Since the field is quickly growing and since there have been many study-to-study variations in terms of choices made by researchers when collecting, processing, and analyzing data, we saw the need to audit this field and formulate a comprehensive view of current temporal network analyses. To systematically chart researchers' practices when conducting temporal network analyses, we reviewed articles conducting temporal network analyses on psychological variables (published until March 2021) in the framework of a scoping review. We identified 43 articles and present the detailed results of how researchers are currently conducting temporal network analyses. A commonality across results concerns the wide variety of data collection and analytical practices, along with a lack of consistency between articles about what is reported. We use these results, along with relevant literature from the fields of ecological momentary assessment and network analysis, to formulate recommendations on what type of data is suited for temporal network analyses as well as optimal methods to preprocess and analyze data. As the field is new, we also discuss key future steps to help usher the field's progress forward and offer a reporting checklist to help researchers navigate conducting and reporting temporal network analyses.
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