This article reviews the current status of methods available for the analysis of psychological change in adulthood and aging. Enormous progress has been made in designing statistical models that can capture key aspects of intraindividual change, as reflected in techniques such as latent growth curve models and multilevel (random-effects) models. However, the rapid evolution of statistical innovations may have obscured the critical importance of addressing rival explanations for statistical outcomes, such as cohort differences or practice effects that could influence estimates of age-related change. Choice of modeling technique and implementation of a specific modeling approach should be grounded in and reflect both the theoretical nature of the developmental phenomenon and the features of the sampling design that selected persons, variables, and contexts for empirical observation.

The authors used 6-year longitudinal data from the Victoria Longitudinal Study (VLS) to investigate individual differences in amount of episodic memory change. Latent change models revealed reliable individual differences in cognitive change. Changes in episodic memory were significantly correlated with changes in other cognitive variables, including speed and working memory. A structural equation model for the latent change scores showed that changes in speed and working memory predicted changes in episodic memory, as expected by processing resource theory. However, these effects were best modeled as being mediated by changes in induction and fact retrieval. Dissociations were detected between cross-sectional ability correlations and longitudinal changes. Shuffling the tasks used to define the Working Memory latent variable altered patterns of change correlations.

Metacognition includes two key concepts: monitoring of internal states and adaptive use of control strategies based on that monitoring. We review studies that indicate that aging does not materially affect the accuracy of elementary forms of monitoring encoding and retrieval states in episodic-memory tasks, even though it does influence episodic memory itself. Spared monitoring accuracy can therefore serve as a basis for older adults' use of compensatory strategies to achieve learning goals despite the influence of aging on mechanisms of learning. Metacognitive-intervention studies based on this premise show greater effects on learning than do traditional strategy-training approaches. Use of strategies for self-regulation, informed by monitoring, may be an important tool for older adults' effective cognitive functioning in everyday life.

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris (1985) were used to evaluate the power to detect slope covariances. Even with large samples (N = 500) and several longitudinal occasions (4 or 5), statistical power to detect covariance of slopes was moderate to low unless growth curve reliability at study onset was above .90. Studies using LGCMs may fail to detect slope correlations because of low power rather than a lack of relationship of change between variables. The present findings allow researchers to make more informed design decisions when planning a longitudinal study and aid in interpreting LGCM results regarding correlated interindividual differences in rates of development.

Everyday leisure activities in adulthood and old age have been investigated with respect to constructs such as successful aging, an engaged lifestyle, and prevention of age-related cognitive decline. They also relate to mental health and have clinical value, as they can inform diagnosis and interventions. In the present study, the authors enhanced the content validity of the Victoria Longitudinal Study activity questionnaire by adding items on physical and social activities and validated a shortened version of the questionnaire. The proposed leisure activity model included 11 activity categories: 3 types of social activities (i.e., activities with close social partners, group-centered public activity, religious activities), physical activities, developmental activities, experiential activities, crafts, game playing, TV watching, travel, and technology use. Confirmatory factor analyses validated the proposed factor structure in 2 independent samples. A higher order model with a general activity factor fitted the activity factor correlations with relatively little loss of fit. Convergent and discriminant validity for the activity scales were supported by patterns of their correlations with education, health, depression, cognition, and personality. In sum, the scores derived from of the augmented Victoria Longitudinal Study activity questionnaire demonstrate good reliability, and validity evidence supports their use as measures of leisure activities in young, middle-aged, and older individuals.