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多层次 SEM 在个体内日常关联长期变化建模中的应用。

Modeling long-term changes in daily within-person associations: An application of multilevel SEM.

机构信息

Department of Psychology.

Department of Psychology, University of California, Davis.

出版信息

Psychol Aging. 2019 Mar;34(2):163-176. doi: 10.1037/pag0000331. Epub 2019 Feb 7.

Abstract

Short-term within-person associations are considered to reflect unique dynamic characteristics of an individual and are frequently used to predict distal outcomes. These effects are typically examined with a 2-step statistical process. The present research demonstrates how long-term changes in short-term within-person associations can be modeled simultaneously within a multilevel structural equation modeling framework. We demonstrate the utility of this model using measurement burst data from the National Study of Daily Experiences (NSDE) embedded within the Midlife in the United States (MIDUS) longitudinal study. Two measurement bursts were separated by 9 years, with each containing daily measures of stress and affect across 8 consecutive days. Measures of life satisfaction and psychological well-being were also assessed across the 9-year period. Three-level structural equation models were fit to simultaneously model short-term within-person associations between stress and negative affect and long-term changes in these associations over the 9-year period. Individual differences in long-term changes of the short-term dynamics between stress and affect predicted well-being levels. We highlight how characterizing individuals based on the strength of their within-person associations across multiple time scales can be informative in predicting distal outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

摘要

短期个体内关联被认为反映了个体独特的动态特征,常用于预测远距离结果。这些效应通常通过两步统计过程进行检验。本研究展示了如何在多层次结构方程模型框架内同时模拟短期个体内关联的长期变化。我们使用嵌入在美国中期生活研究(MIDUS)纵向研究中的国家日常经验研究(NSDE)中的测量爆发数据来证明该模型的实用性。两个测量爆发期相隔 9 年,每个爆发期包含 8 天连续的压力和情绪的日常测量。在 9 年期间还评估了生活满意度和心理健康的测量。三水平结构方程模型拟合同时模拟短期个体内关联在压力和消极情绪之间的短期动态以及这些关联在 9 年期间的长期变化。压力和情感之间短期动态的长期变化的个体差异很好地预测了幸福感水平。我们强调了如何根据个体在多个时间尺度上的个体内关联的强度来对个体进行分类,这对预测远距离结果具有重要意义。

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