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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.(More)
OBJECTIVE Do lifestyle activities buffer normal aging-related declines in cognitive performance? The emerging literature will benefit from theoretically broader measurement of both lifestyle activities and cognitive performance, and longer-term longitudinal designs complemented with dynamic statistical analyses. We examine the temporal ordering of changes(More)
Participants listened to randomly selected excerpts of popular music and rated how nostalgic each song made them feel. Nostalgia was stronger to the extent that a song was autobiographically salient, arousing, familiar, and elicited a greater number of positive, negative, and mixed emotions. These effects were moderated by individual differences (nostalgia(More)
The authors use multiple-sample longitudinal data from different test batteries to examine propositions about changes in constructs over the life span. The data come from 3 classic studies on intellectual abilities in which, in combination, 441 persons were repeatedly measured as many as 16 times over 70 years. They measured cognitive constructs of(More)
Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling(More)
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(More)
Pubertal development is a nonlinear process progressing from prepubescent beginnings through biological, physical, and psychological changes to full sexual maturity. To tether theoretical concepts of puberty with sophisticated longitudinal, analytical models capable of articulating pubertal development more accurately, we used nonlinear mixed-effects models(More)
Multitrait-multimethod (MTMM) confirmatory factor models were combined with longitudinal structural equation models to examine trait and method stability over time. A longitudinal correlated-trait correlated-method (CT-CM) model allowed for the study of trait and method variance in observed scores over time. Longitudinal measurement invariance was examined(More)
Reliabilities of the two most widely used intraindividual variability indicators, ISD (2) and ISD, are derived analytically. Both are functions of the sizes of the first and second moments of true intraindividual variability, the size of the measurement error variance, and the number of assessments within a burst. For comparison, the reliability of the(More)