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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.,

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Test-retest data can reflect systematic changes over varying intervals of time in a "time-lag" design. This article shows how latent growth models with planned incomplete data can be used to separate psychometric components of developmental interest, including internal consistency reliability, test-practice effects, factor stability, factor growth, and state fluctuation. Practical analyses are proposed using a structural equation model for longitudinal data on multiple groups with different test-retest intervals. This approach is illustrated using 2 sets of data collected from students measured on the Woodcock-Johnson-Revised Memory and Reading scales. The results show how alternative time-lag models can be fitted and interpreted with univariate, bivariate, and multivariate data. Benefits, limitations, and extensions of this structural time-lag approach are discussed. Test-retest data are often collected to examine test reliability and trait stability. In the traditional testretest design, participants are measured on a battery of tests and then, at some specific interval of time, the same participants are measured again on the same tests. Test-retest data are often collected over short periods of time to examine the test-retest reliability of a test or a battery of tests (e.g., Stanley, 1971). When data have been collected over longer intervals of time, the stability of the trait is highlighted and the terms longitudinal and panel analyses are used (e.g., see Nesselroade & Baltes, 1979).

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