Evaluating Causal Dominance of CTmeta-Analyzed Lagged Regression Estimates

Rebecca Kuiper*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)951-963
Number of pages13
JournalStructural Equation Modeling
Issue number6
Publication statusPublished - 2021


  • cross-lagged panel model (CLPM)
  • first-order vector autoregressive (VAR(1)) model
  • Meta-analysis
  • model selection


Dive into the research topics of 'Evaluating Causal Dominance of CTmeta-Analyzed Lagged Regression Estimates'. Together they form a unique fingerprint.

Cite this