Ariens Sigert, Ceulemans Eva, Adolf Janne K
KU Leuven, Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Leuven 3000, Belgium.
KU Leuven, Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Leuven 3000, Belgium.
J Psychosom Res. 2020 Jul 21;137:110191. doi: 10.1016/j.jpsychores.2020.110191.
Time series analysis of intensive longitudinal data provides the psychological literature with a powerful tool for assessing how psychological processes evolve through time. Recent applications in the field of psychosomatic research have provided insights into the dynamical nature of the relationship between somatic symptoms, physiological measures, and emotional states. These promising results highlight the intrinsic value of employing time series analysis, although application comes with some important challenges. This paper aims to present an approachable, non-technical overview of the state of the art on these challenges and the solutions that have been proposed, with emphasis on application towards psychosomatic hypotheses. Specifically, we elaborate on issues related to measurement intervals, the number and nature of the variables used in the analysis, modeling stable and changing processes, concurrent relationships, and extending time series analysis to incorporate the data of multiple individuals. We also briefly discuss some general modeling issues, such as lag-specification, sample size and time series length, and the role of measurement errors. We hope to arm applied researchers with an overview from which to select appropriate techniques from the ever growing variety of time series analysis approaches.
密集纵向数据的时间序列分析为心理学文献提供了一个强大的工具，用于评估心理过程如何随时间演变。近期在身心研究领域的应用已经深入了解了躯体症状、生理指标和情绪状态之间关系的动态本质。这些令人鼓舞的结果凸显了采用时间序列分析的内在价值，尽管应用过程伴随着一些重要挑战。本文旨在提供一个易于理解的、非技术性的关于这些挑战及已提出解决方案的技术现状概述，重点是对身心假设的应用。具体而言，我们详细阐述了与测量间隔、分析中使用的变量数量和性质、对稳定和变化过程的建模、并发关系以及将时间序列分析扩展以纳入多个个体的数据相关的问题。我们还简要讨论了一些一般建模问题，如滞后规范、样本大小和时间序列长度以及测量误差的作用。我们希望为应用研究人员提供一个概述，以便他们从不断增加的各种时间序列分析方法中选择合适的技术。
