Preliminary Detection of Relations Among Dynamic Processes With Two-Occasion Data

Most novel analytic methods for longitudinal data are applicable to studies spanning three time-points of data at a minimum, whereas methods for two-occasion data have garnered comparatively little attention. Here, we address this limitation by introducing the two-wave latent change score (2W-LCS) model, a technique appropriate for preliminary detection of relations among dynamic processes with two-occasion data. The 2W-LCS model is well suited for the investigation of hypotheses in which changes in a construct are posited as predictors of changes in another construct. In an empirical illustration using data of elderly Hispanics from the Health and Retirement Study, we demonstrate how the 2W-LCS model provides the best match to theories rooted in changes, and highlight the advantages of this approach over other modeling alternatives (i.e., Little, Preacher, Selig, and Card, 2007; Selig and Preacher, 2009). 2015, Routledge. All rights reserved.

Export Date: 9 September 2015 Article in Press

change scores/difference scores/latent change score model/latent change score model/two-occasion data

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