Evaluating Causal Dominance of CTmeta-Analyzed Lagged Regression Estimates. Issue 6 (2nd November 2021)

• Record Type: Journal Article
• Authors: Kuiper, Rebecca
• Is Part Of: Structural equation modeling. Volume 28:Issue 6(2021)
• Page Start: 951
• Page End: 963
• Publication Date: 2021-11-02
• Subjects: Meta-analysis -- first-order vector autoregressive (VAR(1)) model -- cross-lagged panel model (CLPM) -- model selection; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1823228
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Meta-analysis techniques allow researchers to aggregate effect sizes, like standardized regression estimates, of different studies. Recently, continuous-time meta-analysis (CTmeta) has been developed such that the time-interval dependent lagged-parameter estimates can be properly meta-analyzed. This leads to overall standardized lagged-parameter estimates and their multivariate confidence interval. Often, researchers are not only interested in these overall estimates but also in a specific ordering of them: Many researchers have an a priori expectation regarding the ordering of the predictive strength of the cross-lagged relationships; referred to as causal dominance. For example, a researcher might expect, based on literature or expertise, that the lagged relationship between burnout and work engagement is weaker than the reciprocal lagged relationship. Such a hypothesis can be evaluated with an AIC-type theory-based model selection criterion: GORICA. This paper introduces and illustrates how the GORICA can be applied to CTmeta-analyzed standardized lagged-parameter estimates and demonstrate its performance.