Detecting spurious effects in cross-lagged panel models: Triangulation is not a valid test

Abstract

The cross-lagged panel model (CLPM) is an analytic technique used to examine the reciprocal causal effects of two or more variables assessed on two or more occasions. Although widely used, the CLPM has been criticized for relying on implausible assumptions, the violation of which can often lead to biased estimates of causal effects. Recently, a triangulation method has been proposed to identify spurious effects in simple CLPM analyses (e.g., Sorjonen, Melin, & Melin, 2024). We use simulations and a discussion of the formulas underlying regression coefficients to show that this method does not provide a valid indicator of spuriousness. This method identifies true causal effects as spurious in realistic situations and should not be used to diagnose whether a causal effect estimated from the CLPM is spurious or not. There are clear reasons to doubt causal estimates from the CLPM, but the results of the triangulation method do not add information about whether such estimates are spurious.

Plain language summary

Researchers use quantitative methods to answer questions about whether one variable has a causal effect on another. These methods rely on specific assumptions that may not hold, and when they do not, false evidence for causal effects can be found. Thus, an important part of quantitative research is developing new methods that can help researchers evaluate the performance of existing quantitative techniques. This paper examines one recently proposed method for evaluating results from a widely used model for analyzing longitudinal data: the cross-lagged panel model. We show that this newly proposed method for evaluating results of the cross-lagged panel model does not work as intended and should not be used to evaluate existing research results.