LONG-TERM INCREASES IN DAILY STRESS REACTIVITY PREDICTS WELL-BEING: FINDINGS FROM DAISI MEASUREMENT BURST DATA - Rush, J (Innov Aging, 2018)
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 two-step statistical process, where i) person-specific estimates are extracted from a multilevel model; then ii) these estimates are included as a time-invariant individual difference predictor variable in a subsequent statistical model (e.g., general linear regression, survival model) to predict some other distal outcome (e.g., mental health, mortality). 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 Daily Stress Interview (DAISI) Study. Two measurement bursts were separated by six years, with each containing daily measures of stress and affect. Burst one consisted of thirty daily measurement occasions, whereas burst two consisted of seven. Measures of psychological well-being were also assessed across the six-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 six-year period. Individual differences in long-term changes of the short-term dynamics between stress and affect predicted psychological well-being levels. We highlight how characterizing an individual based on the strength of their within-person associations across multiple time scales can be important predictors of distal outcomes, as well as the utility of employing multilevel structural equation models in conjunction with measurement burst designs.