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日常日记应对数据的多层次因素分析和结构方程建模:特质与状态变化建模

Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation.

作者信息

Roesch Scott C, Aldridge Arianna A, Stocking Stephanie N, Villodas Feion, Leung Queenie, Bartley Carrie E, Black Lisa J

机构信息

San Diego State University.

出版信息

Multivariate Behav Res. 2010 Sep;45(5):767-789. doi: 10.1080/00273171.2010.519276.

Abstract

The current study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data was collected on a large (n = 366) multiethnic sample over the course of five days. Intraclass correlation coefficient for the derived factors suggested approximately equal amounts of variability in coping usage at the state and trait levels. MFAs showed that Problem-Focused Coping and Social Support emerged as stable factors at both the within-person and between-person levels. Other factors (Minimization, Emotional Rumination, Avoidance, Distraction) were specific to the within-person or between-person levels, but not both. Multilevel structural equation modeling (MSEM) showed that the prediction of daily positive and negative affect differed as a function of outcome and level of coping factor. The Discussion section focuses primarily on a conceptual and methodological understanding of modeling state and trait coping using daily diary data with MFA and MSEM to examine covariation among coping variables and predicting outcomes of interest.

摘要

本研究采用每日日记数据的多层次建模方法,对应对变量的个体内(状态)和个体间(特质)成分进行建模。该应用包括引入多层次因素分析(MFA),并比较这些特质/状态因素的预测能力。在五天的时间里,对一个大型(n = 366)多民族样本收集了每日日记数据。推导因素的组内相关系数表明,在状态和特质水平上,应对方式的变异性大致相等。MFA显示,问题聚焦应对和社会支持在个体内和个体间水平上均为稳定因素。其他因素(最小化、情绪反刍、回避、分心)则特定于个体内或个体间水平,而非两者兼具。多层次结构方程建模(MSEM)表明,日常积极和消极情绪的预测因应对因素的结果和水平而异。讨论部分主要聚焦于对使用MFA和MSEM的每日日记数据对应对状态和特质进行建模的概念和方法理解,以检验应对变量之间的协变关系并预测感兴趣的结果。

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