he creation of "best methods" for the analysis of change has been a contin uing concern in longitudinal and developmental research (e.g., Bayley,

Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N ϳ 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion "accelerated" data (e.g.,

PD traits tend to decline steadily in prevalence during adolescence and early adulthood. However, adolescents with PDs often have elevated PD traits as young adults, and the stability of PD traits appears to be similar during adolescence and early adulthood.

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.

This research uses multiple-sample longitudinal data from different test batteries to examine propositions about changes in constructs over the lifespan. The data come from three classic studies on intellectual abilities where, in combination, N=441 persons are repeatedly measures as many as 16 times over 70 years. Cognitive constructs of Vocabulary and Memory were measured using eight different age-appropriate intelligence test batteries, and we explore possible linkage of these scales using Item Response Theory (IRT). We simultaneously estimate the parameters of both IRT and Latent Curve Models (LCM) based on a joint model likelihood approach (i.e., NLMIXED and WINBUGS). Group differences are included in the model to examine potential inter-individual differences in levels and change. The resulting Longitudinal IRT (LIRT) analyses leads to a few new methodological suggestions for dealing with repeated constructs based on changing measurements in developmental studies.