Nilam Ram

Emotional experience improves with age: Evidence based on over 10 years of experience sampling

Recent evidence suggests that emotional well-being improves from early adulthood to old age. This study used experience-sampling to examine the developmental course of emotional experience in a representative sample of adults spanning early to very late adulthood. Participants (N = 184, Wave 1; N = 191, Wave 2; N = 178, Wave 3) reported their emotional states at five randomly selected times each day for a one week period. Using a measurement burst design, the one-week sampling procedure was repeated five and then ten years later. Cross-sectional and growth curve analyses indicate that aging is associated with more positive overall emotional well-being, with greater emotional stability and with more complexity (as evidenced by greater co-occurrence of positive and negative emotions). These findings remained robust after accounting for other variables that may be related to emotional experience (personality, verbal fluency, physical health, and demographic variables). Finally, emotional experience predicted mortality; controlling for age, sex, and ethnicity, individuals who experienced relatively more positive than negative emotions in everyday life were more likely to have survived over a 13 year period. Findings are discussed in the theoretical context of socioemotional selectivity theory.

Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups.

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

Using Simple and Complex Growth Models to Articulate Developmental Change: Matching Theory to Method.

Growth curve modeling has become a mainstay in the study of development. In this article we review some of the flexibility provided by this technique for describing and testing hypotheses about: (1) intraindividual change across multiple occasions of measurement, and (2) interindividual differences in intraindividual change. Through empirical example we demonstrate how linear, quadratic, latent basis, exponential, and multiphase versions of the model can be specified using commonly available SEM/multilevel modeling software and illustrate and discuss how results are obtained and interpreted. Particularly, we underscore the “developmental theory” articulated by each model.

Late-life decline in well-being across adulthood in Germany, the United Kingdom, and the United States : Something is seriously wrong at the end of life

Throughout adulthood and old age, levels of well-being appear to remain relatively stable. However, evidence is emerging that late in life well-being declines considerably. Using long-term longitudinal data of deceased participants in national samples from Germany, the United Kingdom, and the United States, we examined how long this period lasts. In all 3 nations and across the adult age range, well-being was relatively stable over age but declined rapidly with impending death. Articulating notions of terminal decline associated with impending death, we identified prototypical transition points in each study between 3 and 5 years prior to death, after which normative rates of decline steepened by a factor of 3 or more. The findings suggest that mortality-related mechanisms drive late-life changes in well-being and highlight the need for further refinement of psychological concepts about how and when late-life declines in psychosocial functioning prototypically begin. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Decline in Life Satisfaction in Old Age: Longitudinal Evidence for Links to Distance-to-Death

Using 12-year longitudinal data from deceased participants of the Berlin Aging Study (N = 414; age 70-103 years, at first occasion; M = 87 years, SD = 8.13), the authors examined whether and how old and very old individuals exhibit terminal decline in reported life satisfaction at the end of life. Relative to age-related decline, mortality-related decline (i.e., distance-to-death) accounted for more variance in interindividual differences in life satisfaction change and revealed steeper average rates of decline, by a factor of 2. By applying change-point growth models, the authors identified a point, about 4 years before death, at which decline showed a two-fold increase in steepness relative to the preterminal phase. For the oldest old (85+ years), a threefold increase was observed. Established mortality predictors, including sex, comorbidities, dementia, and cognition, accounted for only small portions of interindividual differences in mortality-related change in life satisfaction. The authors conclude that late-life changes in subjective well-being are related to mechanisms predicting death and suggest routes for further inquiry.

Life Satisfaction Shows Terminal Decline in Old Age: Longitudinal Evidence from the German Socio-Economic Panel Study (SOEP).

Longitudinal data spanning 22 years, obtained from deceased participants of the German Socio-Economic Panel Study (SOEP; N = 1,637; 70- to 100-year-olds), were used to examine if and how life satisfaction exhibits terminal decline at the end of life. Changes in life satisfaction were more strongly associated with distance to death than with distance from birth (chronological age). Multiphase growth models were used to identify a transition point about 4 years prior to death where the prototypical rate of decline in life satisfaction tripled from -0.64 to -1.94 T-score units per year. Further individual-level analyses suggest that individuals dying at older ages spend more years in the terminal periods of life satisfaction decline than individuals dying at earlier ages. Overall, the evidence suggests that late-life changes in aspects of well-being are driven by mortality-related mechanisms and characterized by terminal decline.

Nonlinear growth curves in developmental research.

Developmentalists are often interested in understanding change processes, and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood.

Nonlinear Growth Models in Mplus and SAS

Nonlinear growth curves or growth curves that follow a specified nonlinear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this article we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the nonlinear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data, collected as part of a study examining the effects of preschool instruction on academic gain, we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included.

Studying Intraindividual Variability: What We Have Learned That Will Help Us Understand Lives in Context

We examine some features of intraindividual variability and the research outcomes from its study. Selected current modeling techniques focused on individual-level analyses are briefly discussed, including some promising applications stemming from dynamical systems theory work. We turn these ideas into issues prominent in the study of behavioral development and examine how the general intraindividual variability orientation can strengthen the further study of lives in context.

Automatic search for fMRI connectivity mapping: An alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM

Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.