Fumiaki Hamagami

Age-related change in personality disorder trait levels between early adolescence and adulthood: a community-based longitudinal investigation.

Objective: To investigate change in personality disorder (PD) traits between early adolescence and early adulthood among individuals in the community.

Modeling incomplete longitudinal and cross-sectional data using latent growth structural models

In this paper we describe some mathematical and statistical models for identifying and dealing with changes over age. We concentrate specifically on the use of a latent growth structural equation model approach to deal with issues of: (1) latent growth models of change, (2) differences in longitudinal and cross-sectional results, and (3) differences due to longitudinal attrition. This is a methodological paper using simulated data, but we base our models on practical and conceptual principles of modeling change in developmental psychology. Our results illustrate both benefits and limitations using structural models to analyze incomplete longitudinal data.

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 the dynamic hypotheses of Gf–Gc theory using longitudinal life-span data

Abstract This research uses longitudinal data from the Wechsler Adult Intelligence Scale (WAIS) and linear structural equation models (e.g., LISREL) in an evaluation of the structural, kinematic, and dynamic hypotheses of the “theory of fluid and crystallized intelligence.” In a first set of analyses we use linear dynamic models in a formal evaluation of the growth and declines of abilities through latent growth and linear dynamic models. Our first results indicate separate trends over age for different intellectual abilities including broad knowledge, spatial reasoning, perceptual speed, and immediate memory. In a second set of analyses we extend these multivariate dynamic structural equation models to explore the age-based leading and lagging indicators. These results indicate a complex system of relationships, with memory losses as an important leading indicator. In a third set of analyses we use confirmatory techniques to test specific aging hypotheses. These results indicate support for both the “general memory loss” hypothesis and the “general slowing” hypothesis, provide some support for the “investment theory” at the adult level, and also suggest a single “general” factor does not describe the complexity of cognitive aging. These result synthesize prior WAIS studies and provide methods for further research on the dynamics of the growth and decline of intellectual abilities across the adult life-span.

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.

Bayesian Analysis of Longitudinal Data Using Growth Curve Models.

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method.

Modeling Latent Growth Curves With Incomplete Data Using Different Types of Structural Equation Modeling and Multilevel Software.

This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in the case of models of latent growth fitted to incomplete data. The general purpose of this article is to provide a demonstration that integrates programming features from different software. The most immediate goal is to help researchers implement these LGC models as a useful way to test hypotheses of growth.

A contemporary method for developmental‐genetic analyses of age changes in intellectual abilities

The purpose of this article is to describe a methodology for the evaluation of biometric genetic hypotheses in the context of a developmental model of growth and change. Linear structural equation models are described for longitudinal and twin data, including aspects of subject attrition and practice effects. These models are applied to 2 variables measured at several points in time in the New York Twin Study (Jarvik, Kallman, & Falek, 1962; Kallman, Feingold, & Bondy, 1951). The patterns of psychometric and biometric changes are different for the 2 intellectual variables. Substantive results are discussed in relation to gf/gc theory (Cattell, 1971; Horn, 1988), and some methodological limitations are emphasized.

Structural Equation Models for Evaluating Dynamic Concepts Within Longitudinal Twin Analyses

A great deal of prior research using structural equation models has focused on longitudinal analyses and biometric analyses. Some of this research has even considered the simultaneous analysis of both kinds of analytic problems. The key benefits of these kinds of analyses come from the estimation of novel parameters, such as the heritability of changes. This paper discusses some recent extensions of longitudinal multivariate models that can be informative within biometric designs. In the methods section we review a previous latent growth structural equation analysis of the New York Twin (NYT) longitudinal data (from McArdle et al., 1998). In the models section we recast this growth model in terms of latent difference scores, add several new dynamic components, including coupling parameters, and consider biometric components and examine model stability. In the results section we present new univariate and bivariate dynamic estimates and tests of various dynamic hypotheses for the NYT data, and we consider a few ways to interpret the age-related biometric components of these models. In the discussion we consider our limitations and present suggestions for future dynamic-genetic research.

Genetic variance in processing speed drives variation in aging of spatial and memory abilities.

Previous analyses have identified a genetic contribution to the correlation between declines with age in processing speed and higher cognitive abilities. The goal of the current analysis was to apply the biometric dual change score model to consider the possibility of temporal dynamics underlying the genetic covariance between aging trajectories for processing speed and cognitive abilities. Longitudinal twin data from the Swedish Adoption/Twin Study of Aging, including up to 5 measurement occasions covering a 16-year period, were available from 806 participants ranging in age from 50 to 88 years at the 1st measurement wave. Factors were generated to tap 4 cognitive domains: verbal ability, spatial ability, memory, and processing speed. Model-fitting indicated that genetic variance for processing speed was a leading indicator of variation in age changes for spatial and memory ability, providing additional support for processing speed theories of cognitive aging.