密集追踪成对数据分析的模型建构探索

doi:10.3724/SP.J.1042.2024.01450

摘要/Abstract

摘要： 成对研究以具有相互作用的两名个体为基本单元, 广泛用于心理学领域涉及人际交互的研究中。它与密集追踪设计的结合有助于探究人际互动过程中个体行为与人际效应的动态变化, 但目前尚缺乏能有效结合密集追踪数据特点以回答成对研究关心问题的统计方法。本研究拟通过模拟和实证研究, 基于动态结构方程模型(Dynamic Structural Equation Modeling, DSEM)框架, 探究三种人际互动模式(双人交互, 一人与多人交互, 多人两两交互)下密集追踪成对数据分析的统计模型的建构、拓展和应用, 推动心理学研究更深入、科学地描述和解释人际交互情境中个体行为的动态发展过程及人际效应。

关键词: 密集追踪数据, 成对研究设计, 模型建构, 动态结构方程模型

Abstract: Dyadic studies, in which two persons interacting with each other (called a dyad) are the fundamental unit of analysis, are widely used in psychological studies involving interpersonal phenomena. The integration of such studies with intensive longitudinal designs helps to further investigate the dynamics of both individual behaviors and interpersonal effects during social interactions. However, due to the data complexity, there is a lack of appropriate statistical approaches that can adequately answer the dyadic research questions of interest based on the characteristics of intensive longitudinal data.
Drawing on the Dynamic Structural Equation Modeling (DSEM) framework for intensive longitudinal data from independent individuals, this study focuses on the construction, extension, and application of statistical models for intensive longitudinal data with different dyadic designs, which can describe and explain the dynamics of individual behaviors and interpersonal relationships in different social interaction contexts, as well as their between-person differences. Specifically, the main contents of this study are as follows. Study 1 focuses on the standard dyadic design in which information is collected from two persons who are interdependent (e.g., couples, siblings, friends). It aims to improve the existing extension of the actor-partner interdependence model in DSEM to better characterize the relationships between two dyad members, and more importantly, to compare and select the appropriate detrending approach based on the improved model, as DSEM assumes stationarity of variables but time trends, a possible source of non-stationarity, are common in longitudinal studies. We conjecture that the detrending practice based on the residual DSEM (RDSEM) framework would work well. Study 2 focuses on the one-with-many (OWM) design for examining features of multiple dyadic relationships, in which one set of focal persons (e.g., therapists, physicians) has with others (e.g., multiple clients, multiple patients). For the intensive OWM design, Study 2 is intended to develop an appropriate statistical model, as well as the corresponding parameter estimation algorithm, to examine the dynamic processes of multiple interpersonal relationships of focal persons. Specifically, a model assuming no time trends will first be constructed. Then it will be integrated with a detrending method based on the conclusion of Study 1 to be applicable to more complex data with time trends. Study 3 turns to a more complex dyadic context, that is, round-robin data, in which every member of a group has judged all other group members and is also judged by every group member. This study aims to develop an appropriate statistical model for the intensive longitudinal round-robin data, as well as the corresponding parameter estimation algorithm, and then to extend the model to data with time trends. Study 4 illustrates the application of the constructed or extended models under three intensive longitudinal dyadic designs through empirical data.
Advances in data collection techniques and the deepening of research questions have posed new challenges to statistical analysis methods. The unique contribution of this research is to develop and extend a series of methodological tools for the newly emerging intensive longitudinal dyadic data. Considering the diversity of interpersonal interaction contexts, we consider three common dyadic designs. In addition, detrending is a non-negligible issue for the application of DSEM but has not yet been fully investigated. This research makes an exploration for detrending in dyadic contexts. Moreover, we plan to develop a user-friendly software package to include all the developed and extended models in this research, so as to provide a basis for the application of intensive longitudinal dyadic data analysis. This research will help applied researchers to gain a deeper and more scientific understanding of changes in individual behaviors and interpersonal effects in the context of social interactions, and to promote theoretical development in related fields.

Key words: intensive longitudinal data, dyadic designs, model construction, dynamic structural equation modeling

中图分类号:

B841

肖悦, 刘红云, 徐永泽. (2024). 密集追踪成对数据分析的模型建构探索. 心理科学进展 , 32(9), 1450-1462.

XIAO Yue, LIU Hongyun, XU Yongze. (2024). Model construction for intensive longitudinal dyadic data analysis. Advances in Psychological Science, 32(9), 1450-1462.

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