Detecting Spurious Effects in Cross-Lagged Panel Models: Triangulation is Not a Valid Test

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. , @sorjonenₛpurious₂024. 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.