Understanding the dynamic nature of well-being: a multilevel SEM framework to capture intra- and inter-individual associations across multiple timescales and levels of analysis
Date
2018-05-18
Authors
Rush, Jonathan
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Abstract
The study of well-being has a long history of investigation from a nomothetic (between-person) perspective that aimed to understand characteristic levels of well-being and individual difference variables that account for stable differences across people. Recent investigations have demonstrated that levels of well-being have the capacity to rapidly fluctuate within people over short intervals and also exhibit slower changes over longer intervals, highlighting the importance of considering the ideographic (within-person) nature of well-being. The aim of this dissertation was to help build on such within-person understanding by demonstrating how theories of well-being may be empirically evaluated using innovative research designs (e.g., intensive repeated measurement designs) and analytic techniques (e.g., multilevel structural equation models [MSEM]) that can fully capture the complexity and dynamic nature of well-being. Three distinct research studies employing intensive repeated measurement designs and an MSEM analytic framework addressed a variety of research questions concerning intra- and inter-individual predictors of well-being. Study one (Chapter 2) simultaneously examined the multilevel moderation and mediation effects of cognitive interference on stress reactivity estimated in a 14-day daily diary design. Study two (Chapter 3) utilized measurement burst data from a large U.S. sample of adults, assessed across multiple time-scales, to examine long-term changes in short-term within-person associations. Random within-person slopes were specified as exogenous predictor variables of individual differences in global levels of psychological well-being. Study three (Chapter 4) used simulation data to examine the conditions where specifying within-person measurement scales as latent variables compared to unit-weighted composite scores optimized detection of within-person effects. This dissertation demonstrates the importance of innovative design and analysis to appropriately model and understand the complex, dynamic associations that operate within and across individuals in predicting their experiences of well-being.
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Keywords
multilevel structural equation modeling, stress reactivity, intensive measurement designs, well-being