Effect size guidelines for cross-lagged effects.

Ulrich Orth · Laurenz Linus Meier · Janina Larissa Bühler · Laura C. Dapp · Samantha Krauss · Denise Messerli +1 more

737 citations0/0 files storedPsychological Methods

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Meta-analysis and systematic reviews Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference

Abstract

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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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References

2009Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement 1997Bias in meta-analysis detected by a simple, graphical test 1992A power primer.2010Conducting Meta-Analyses in R with the metafor Package 2011Sources of Method Bias in Social Science Research and Recommendations on How to Control It 2009Introduction to Meta‐Analysis 2016Effect size guidelines for individual differences researchers 2008Latent Variable Modeling of Differences and Changes with Longitudinal Data

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