密集追踪数据分析：模型及其应用

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。

Intensive longitudinal data analysis: Models and application

In the fields of psychology, education, and clinical science, researchers have devoted increasing attention to the intraindividual dynamics of behaviors, minds, and treatment effects over time, making personalized modeling a growing concern. Traditional cross-sectional and longitudinal studies only have a few measurement time points for each individual, which is not suitable for studying intraindividual dynamics. Intensive longitudinal design collects a set of measures from individuals at multiple time points with higher frequency over longer periods. With its strengths in more immediate, accurate, and authentic assessments, this design is more suitable to investigate the dynamics and mechanisms of intraindividual processes. With the development of mobile phones and other mobile devices, researchers can conveniently collect intensive longitudinal data for various aspects of psychology, including individual emotion, personality, cognition, and behavior patterns. The intensive longitudinal design has recently become one of the most prominent and promising approaches in psychological research, but most of these studies still relied on traditional analyzing methods. We first reviewed a conventional method of intensive longitudinal data analysis, the multilevel linear model (MLM), and discussed its limitations in analyzing intensive longitudinal data. We then introduced the principles, empirical applications, strengths, and weaknesses of two advanced modeling methods, dynamic structural equation model (DSEM) and group iterative multiple model estimation (GIMME). DSEM is a top-down approach of modeling intensive longitudinal data while GIMME is a bottom-up one, both being implemented in commonly used software. DSEM is one of the most promising methods for intensive longitudinal modeling and can be regarded as a multilevel extension of the dynamic factor model (DFM). It combines the strengths of various modeling approaches, including multilevel modeling, time-series modeling, structural equational model (SEM), and time-varying effects modeling (TVEM). GIMME is a dynamic network method initially proposed for functional magnetic resonance imaging (fMRI) data analysis and has recently been applied to intensive longitudinal data analysis. It combines individual- and group-level information to estimate network models at both levels, bridging nomothetic (population) and idiographic (individual) approaches to intensive longitudinal data analysis. By introducing these two advanced modeling methods, the current review can help deepen the understanding of the top-down approach and bottom-up approach and clarify their strengths and weaknesses in the intensive longitudinal data analysis. To help empirical researchers better understand the modeling of DSEM and GIMME and show the advantages of the two models compared with MLM, we provided a tutorial on how to analyze the intensive longitudinal data with the three models (i.e., MLM, DSEM, and GIMME), respectively. We presented the analyzing processes step by step and explained how to interpret the results of these models accordingly. By comparing the output results of the three models, the current review summarized the characteristics of each model. The corresponding Mplus and R codes were provided in the appendixes. Along with the three modeling methods mainly introduced in the current review, we also provided a general introduction of other common modeling methods in the intensive longitudinal data analysis. The current review summarized the popular models in the intensive longitudinal data analysis on their strengths and weaknesses and guided researchers to select suitable modeling methods in different situations. The current review contributes to the development and application of the advanced methods of intensive longitudinal data analysis and helps researchers better understand the dynamic process behind the intensive longitudinal data.