Abstract
Objectives: This article provides an example and application of growth curve analysis for modeling individual differences in behavioral rates of change in aging. The latent curve modeling approach to the analysis of change allows researchers to describe change as a continuous process and to address issues related to individual differences in change over time. Methods: Data are used from the Longitudinal Study of Aging (LSOA) on change in activities of daily living (ADLs) in the elderly. Analyses involved direct maximum likelihood estimation using complete and incomplete cases. Results: It is possible to statistically capture developmental changes. Change in participants’ ADLs was characterized by a negative linear trajectory, and there was evidence of significant individual variability in the starting point of the trajectory and the rate of change over time. Discussion: The article discusses the utility of latent curve analysis in aging research as well as other techniques that are extensions of latent curve analysis.
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