Expanding test–retest designs to include developmental time-lag components.

Test-retest data can reflect systematic changes over varying intervals of time in a 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.