人气
心理学研究
心理学
过程(计算)
透视图(图形)
因果推理
推论
数据科学
纵向数据
认知心理学
计算机科学
社会心理学
人工智能
医学
数据挖掘
病理
操作系统
标识
DOI:10.1146/annurev-clinpsy-081423-022947
摘要
Research based on intensive longitudinal data (ILD)—consisting of many repeated measures from one or multiple individuals—is rapidly gaining popularity in psychological science. To appreciate the unique potential of ILD research for clinical psychology, this review begins by examining how our three traditional research approaches fall short when the goal is to investigate processes. It then explores how the analysis of ILD can be used to study a process as it unfolds within a specific person over time but also to study average process features or individual differences therein. By emphasizing the alignment between research questions, data collection, and analytical strategies, the potential of ILD research is further highlighted. It is argued that for future progress it is essential to integrate machine learning and causal inference methods with statistical techniques for ILD and to become more explicit about timescales, time frames, and dynamics in psychological theories.
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