Kevin J. Grimm

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.

Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators.

Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of childrens developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.

Music-Evoked Nostalgia: Affect, Memory, and Personality

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 proneness, mood state, dimensions of the Affective Neurosciences Personality Scale, and factors of the Big Five Inventory). Nostalgia proneness predicted stronger nostalgic experiences, even after controlling for other individual difference measures. Nostalgia proneness was predicted by the Sadness dimension of the Affective Neurosciences Personality Scale and Neuroticism of the Big Five Inventory. Nostalgia was associated with both joy and sadness, whereas nonnostalgic and nonautobiographical experiences were associated with irritation.

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.

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.

Modeling Life-Span Growth Curves of Cognition Using Longitudinal Data with Multiple Samples and Changing Scales of Measurement.

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 vocabulary and memory using 8 age-appropriate intelligence test batteries and explore possible linkage of these scales using item response theory (IRT). They simultaneously estimated the parameters of both IRT and latent curve models based on a joint model likelihood approach (i.e., NLMIXED and WINBUGS). They included group differences in the model to examine potential interindividual differences in levels and change. The resulting longitudinal invariant Rasch test analyses lead to a few new methodological suggestions for dealing with repeated constructs based on changing measurements in developmental studies.

Do changes in lifestyle engagement moderate cognitive decline in normal aging? Evidence from the Victoria Longitudinal Study.

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 in lifestyle activities and changes in cognitive neuropsychological performance in older adults. METHOD We assembled data (n = 952) across a 12-year (5-wave) period from the Victoria Longitudinal Study. Latent change score models were applied to examine whether (and in which temporal order) changes in physical, social, or cognitive lifestyle activities were related to changes in three domains of cognitive performance. RESULTS Two main results reflect the dynamic coupling among changes in lifestyle activities and cognition. First, reductions in cognitive lifestyle activities were associated with subsequent declines in measures of verbal speed, episodic memory, and semantic memory. Second, poorer cognitive functioning was related to subsequent decrements in lifestyle engagement, especially in social activities. CONCLUSIONS The results support the dual contention that (a) lifestyle engagement may buffer some of the cognitive changes observed in late life, and (b) persons who are exhibiting poorer cognitive performance may also relinquish some lifestyle activities.

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.

Multivariate Longitudinal Methods for Studying Developmental Relationships between Depression and Academic Achievement.

Recent advances in methods and computer software for longitudinal data analysis have pushed researchers to more critically examine developmental theories. In turn, researchers have also begun to push longitudinal methods by asking more complex developmental questions. One such question involves the relationships between two developmental processes. In this situation, choosing a longitudinal method is not obvious and should depend on specific hypotheses and research questions. This article outlines three common bivariate longitudinal models, including the bivariate latent growth curve model, the latent growth curve with a time-varying covariate, and the bivariate dual change score growth model, and illustrates their use by modeling how the development of depression is related to the development of achievement. Each longitudinal model is fitted to repeated measurements of childrens depression and achievement from the National Longitudinal Survey of Youth (NLSY) data set in order to examine differing developmental relationships, and show how the developmental questions are answered by each longitudinal technique. The results from the longitudinal models appear to be somewhat at odds with one another regarding the developmental relationships between achievement and depression, but the conclusions are actually correct solutions to different developmental questions. These results highlight the need for researchers to match their research questions with model selection.

Testing a developmental cascade model of emotional and social competence and early peer acceptance.

A developmental cascade model of early emotional and social competence predicting later peer acceptance was examined in a community sample of 440 children across the ages of 2 to 7. Childrens externalizing behavior, emotion regulation, social skills within the classroom and peer acceptance were examined utilizing a multitrait-multimethod approach. A series of longitudinal cross-lag models that controlled for shared rater variance were fit using structural equation modeling. Results indicated there was considerable stability in childrens externalizing behavior problems and classroom social skills over time. Contrary to expectations, there were no reciprocal influences between externalizing behavior problems and emotion regulation, although higher levels of emotion regulation were associated with decreases in subsequent levels of externalizing behaviors. Finally, childrens early social skills also predicted later peer acceptance. Results underscore the complex associations among emotional and social functioning across early childhood.