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) m...

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Bibliographic Details
Published inStructural equation modeling Vol. 23; no. 2; pp. 180 - 193
Main Authors Henk, Corinne M., Castro-Schilo, Laura
Format Journal Article
LanguageEnglish
Published Hove Routledge 03.03.2016
Psychology Press
Subjects
change scores
Data analysis
difference scores
Hispanic people
latent change score model
Older people
Social research
two-occasion data
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Summary: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, & Card, 2007; Selig & Preacher, 2009).
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2015.1030022