Epskamp Sacha, van Borkulo Claudia D, van der Veen Date C, Servaas Michelle N, Isvoranu Adela-Maria, Riese Harriëtte, Cramer Angélique O J
Department of Psychological Methods, University of Amsterdam.
Department of Psychiatry, Interdisciplinary Center for Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen.
Clin Psychol Sci. 2018 May;6(3):416-427. doi: 10.1177/2167702617744325. Epub 2018 Jan 19.
Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a , in which one investigates if symptoms (or other relevant variables) predict one another over time, and a , in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.
（a）心理学的网络视角；（b）时间序列数据的收集，以捕捉日常生活中的症状波动和其他随时间变化的因素。将这些趋势结合起来可以估计个体内部的网络结构。我们认为，这些网络可以作为产生假设的结构直接应用于临床研究和实践。可以计算两种网络：一种是研究症状（或其他相关变量）是否随时间相互预测的网络，另一种是研究症状在同一测量窗口内是否相互预测的网络。同期网络是一个偏相关网络，它在横断面数据分析中出现，但尚未用于时间序列数据分析。我们解释了偏相关网络的重要性，并以一名精神病患者的时间序列数据为例说明了网络结构。
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