Effect Size Guidelines for Cross-Lagged Effects

Cross-lagged models are by far the most commonly used method to test the prospective effect of one construct on another, yet there are no guidelines for interpreting the size of cross-lagged effects. This research aims to establish empirical benchmarks for cross-lagged effects, focusing on the cross-lagged panel model (CLPM) and the random intercept cross-lagged panel model (RI-CLPM). We drew a quasirepresentative sample of studies published in four subfields of psychology (i.e., developmental, social-personality, clinical, and industrial-organizational). The dataset included 1,028 effect sizes for the CLPM and 302 effect sizes for the RI-CLPM, based on data from 174 samples. For the CLPM, the 25th, 50th, and 75th percentiles of the distribution corresponded to cross-lagged effect sizes of .03, .07, and .12, respectively. For the RI-CLPM, the corresponding values were .02, .05, and .11. Effect sizes did not differ significantly between the CLPM and RI-CLPM. Moreover, effect sizes did not differ significantly across subfields and were not moderated by design characteristics. However, effect sizes were moderated by the concurrent correlation between the constructs and the stability of the predictor. Based on the findings, we propose to use .03 (small effect), .07 (medium effect), and .12 (large effect) as benchmark values when interpreting the size of cross-lagged effects, for both the CLPM and RI-CLPM. In addition to aiding in the interpretation of results, the present findings will help researchers plan studies by providing information needed to conduct power analyses and estimate minimally required sample sizes. Translational Abstract Researchers in psychology and related disciplines often use longitudinal data to examine the effect of a construct measured at one point in time on another construct measured at a later time point. This article provides guidelines for interpreting the size of these prospective effects. We focused on two frequently used models: the cross-lagged panel model (CLPM) and the random intercept cross-lagged panel model (RI-CLPM). We examined the range of effect sizes reported for these models in a quasirepresentative sample of published articles drawn from four subfields of psychology (developmental, social-personality, clinical, and industrial-organizational). Average effect sizes were similar for the CLPM and RI-CLPM and did not differ significantly across subfields. Based on the findings, we recommend that researchers use .03 (small effect), .07 (medium effect), and .12 (large effect) as benchmark values when interpreting the size of cross-lagged effects for both the CLPM and RI-CLPM.

Automated summary

This study aims to provide empirical benchmarks for cross-lagged effects and found that the benchmark values for cross-lagged effects in the cross-lagged panel model and the random intercept cross-lagged panel model are 0.03, 0.07, and 0.12. The effect sizes did not differ significantly between different subfields and design characteristics.