Recent research investigating the course of affective development across the adult life span has incorporated both cross-sectional and longitudinal data in analyses to understand the aging-affect relationship. Most of these studies, however, have not provided an empirical test to determine whether the cross-sectional and longitudinal data can be combined to infer developmental processes. Utilizing an age heterogeneous sample followed over a 10-year span (N = 1,019, M = 54.14 ± 13.06), the present study used an accelerated longitudinal design to investigate whether cross-sectional age differences could be found in longitudinal aging trajectories of positive affect (PA), negative affect (NA), and their confluence (i.e., affect optimization, the experience of PA relative to NA). Additionally, age-related differences in poignancy, co-occurrences of PA and NA, were examined. Absence of cross-sectional age-differences in the estimated longitudinal aging trajectories of PA and affect optimization suggested that a developmental process could be inferred; whereas, the longitudinal aging trajectories for NA showed cross-sectional age differences. PA and affect optimization showed a cubic relationship with age; NA showed decreases across adulthood; and poignancy showed age-related increases across adulthood. Self-rated health was investigated as a covariate in all models. Though somewhat more nuanced, the estimated trajectories for PA, NA, affect optimization, and poignancy provided support for theories of affective aging. The implications of these findings, directions for future research, and issues surrounding using cross-sectional data to infer developmental change are discussed. (PsycINFO Database Record

We need to understand how psychosocial resources develop, identify the influences that threaten their maintenance, detect the circumstances under which these resources are used, and elucidate the factors that support and promote their growth. Three important components to studying the development of resilience include its dynamic nature, context, and timescale of measurement. Dynamic systems (DS) approaches focus on physiological and psychological structures underling the development of resilience by explicitly mapping parameters of change onto their corresponding aspects of functioning. Previous research has captured emotion regulation within individuals, across traits, and in close personal relationships to show how these methods depict dynamic regulation/resilience resources and their influence on outcomes of interest. The use of multi-time scaled data informs how daily emotion regulation is disrupted in the context of stress to produce dysregulation and disease later in the life course. This approach can also reveal how resilience resources counteract these adverse processes and allow others to thrive and be well. Researchers must not only explore short-term variation in constructs of interest, but also explore how these shorter-term fluctuations contribute to longer-term changes. The confluence of DS, contextual influences, and multiple timescales provides an important set of tools to better understand development.

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To characterize the stress regulation system, we use a reservoir to reflect how much stress an individual “holds” over time. Factors affecting what is contained in a stress reservoir are incoming stress (Input), accumulation/dissipation (Strdiss), and actions taken to discharge stress (e.g., Control). At the within person level, time-varying control predicts better Strdiss (β= -0.03±0.01, p <.001), even when controlling for between person differences (e.g., age, neuroticism) and between and within person impacts of Input. Thus, control reflects an important stress dissipation tool. Further analyses indicated a significant 2-way interaction between time-varying effects of Input and Control (β= 0.14±0.03, p <.0001) and Strdiss and Control (β= 0.60±0.18, p <.001) on self-reported health and a significant 3-way time-varying interaction of Input, Strdiss and Control on depression (β= -0.173±0.07, p <.012). Studies of this type move beyond the static assessments of risk and resilience to a more dynamic one.

Although prominent theories of intimate relationships, and couples themselves, often conceive of relationships as fluctuating widely in their degree of closeness, longitudinal studies generally describe partners’ satisfaction as stable and continuous or as steadily declining over time. The increasing use of group-based trajectory models (GBTMs) to identify distinct classes of change has reinforced this characterization, but these models fail to account for individual differences within classes and within-person variability across classes and may thus misrepresent how couples’ satisfaction changes. The goal of the current analyses was to determine whether accounting for these additional sources of variance through growth mixture models (GMMs) alters characterizations of satisfaction changes over time. Applied to longitudinal data from 12 independent studies of first-married couples (combined N = 1,249 couples), GMMs that allowed for class-specific individual differences and within-person variability fit the data better than the GBTMs that constrained these to be equal across classes. Most notably, considerable within-person variability was evident within each class, consistent with the idea that spouses do indeed fluctuate in their satisfaction. Spouses who dissolved their marriages were 3.8–5.7 times more likely to be in classes characterized by greater volatility in satisfaction. Because the early years of marriage appear to be characterized by within-person fluctuations in satisfaction, time-varying correlates of these fluctuations are likely to be at least as important as time-invariant correlates in explaining why some marriages thrive where others falter.