密集追踪数据分析:模型及其应用

Intensive longitudinal data analysis:Models and application

在心理学、教育学和临床医学等领域,越来越多的研究者开始关注个体内部的行为、心理、临床效果等随时间而产生的动态变化,重视针对个体的差异化建模。密集追踪是一种在短时间内对个体进行多个时间节点密集追踪测量的方法,更适合用于研究个体内部心理过程等的动态变化及其作用机制。近年来,密集追踪成为心理学研究的一大热点,但许多密集追踪的研究分析仍停留在较为传统的方法。方法学领域已涌现出较多用于密集追踪数据分析的模型方法,较为主流的模型包括以动态结构方程模型(Dynamic Structural Equation Model,DSEM)为代表的自上而下的建模方法,以及以组迭代多模型估计(Group Iterative Multiple Model Estimation,GIMME)为代表的自下而上的建模方法。二者均可以方便地对密集追踪数据中的自回归及交叉滞后效应进行建模。

In the fields of psychology,education,and clinical science,researchers have devoted increased attention to the dynamic changes and personalized modeling of individuals'behaviors,minds,and treatment effects over time.Intensive longitudinal data is a set of measures collected at multiple time points with higher frequency over shorter periods.Thus,it can be used in the analysis of the dynamics and mechanisms of within-person processes.In recent years,intensive longitudinal design has become one of the most prominent and promising approaches in psychological research.However,many of these researches still rely on traditional data analysis methods.Many models have been proposed to analyze intensive longitudinal data,including top-down approaches(e.g.,dynamic structural equation model,DSEM)and bottom-up approaches(e.g.,group iterative multiple model estimation,GIMME).Both of the methods can conveniently model autoregressive and cross-lagged effects in intensive longitudinal data.