Inferring Longitudinal Relationships Between Variables: Model Selection Between the Latent Change Score and Autoregressive Cross-Lagged Factor Models. Issue 3 (3rd May 2016)

• Record Type: Journal Article
• Authors: Usami, Satoshi; Hayes, Timothy; McArdle, John J.
• Is Part Of: Structural equation modeling. Volume 23:Issue 3(2016)
• Page Start: 331
• Page End: 342
• Publication Date: 2016-05-03
• Subjects: autoregressive cross-lagged model -- causal inference -- latent change score model -- longitudinal data -- model selection; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2015.1066680
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : This research focuses on the problem of model selection between the latent change score (LCS) model and the autoregressive cross-lagged (ARCL) model when the goal is to infer the longitudinal relationship between variables. We conducted a large-scale simulation study to (a) investigate the conditions under which these models return statistically (and substantively) different results concerning the presence of bivariate longitudinal relationships, and (b) ascertain the relative performance of an array of model selection procedures when such different results arise. The simulation results show that the primary sources of differences in parameter estimates across models are model parameters related to the slope factor scores in the LCS model (specifically, the correlation between the intercept factor and the slope factor scores) as well as the size of the data (specifically, the number of time points and sample size). Among several model selection procedures, correct selection rates were higher when using model fit indexes (i.e., comparative fit index, root mean square error of approximation) than when using a likelihood ratio test or any of several information criteria (i.e., Akaike's information criterion, Bayesian information criterion, consistent AIC, and sample-size-adjusted BIC).