Drawing Conclusions from Cross-Lagged Relationships: Re-Considering the Role of the Time-Interval

By Rebecca M. Kuiper, O. Ryan. 2018

131 citations recorded by the source corpus.

The cross-lagged panel model (CLPM), a discrete-time (DT) SEM model, is frequently used to gather evidence for (reciprocal) Granger-causal relationships when lacking an experimental design. However, it is well known that CLPMs can lead to different parameter estimates depending on the time-interval of observation. Consequently, this can lead to researchers drawing conflicting conclusions regarding the sign and/or dominance of relationships. Multiple authors have suggested the use of continuous-time models to address this issue. In this article, we demonstrate the exact circumstances under which such conflicting conclusions occur. Specifically, we show that such conflicts are only avoided in general in the case of bivariate, stable, nonoscillating, first-order systems, when comparing models with uniform time-intervals between observations. In addition, we provide a range of tools, proofs, and guidelines regarding the comparison of discrete- and continuous-time parameter estimates.

Subfields: Cognitive Neuroscience, Economics and Econometrics, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/7e17e35215d3a3b8b521a358095c16a023a6f4d6

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