Fitting RI-CLPM Is Not Enough: Diagnostic Sensitivity and Reporting Practices in Within-Person Longitudinal Research

Abstract

The random-intercept cross-lagged panel model (RI-CLPM) is widely used to separate stable between-person differences from within-person dynamics. Yet fitting an RI-CLPM does not guarantee that the data can support meaningful within-person inference. We introduce the concept of RI-CLPM readiness, defined by measurement comparability and diagnostic sensitivity. Using Monte Carlo simulations with powRICLPM, we show that power to detect within-person cross-lagged effects depends jointly on reliability, ICC, number of waves, sample size, and target effect size; high ICC, modest reliability, and few waves can substantially reduce power even in large samples. We then review reporting practices in 186 empirical RI-CLPM applications. Many studies reported sample size, wave count, and reliability, but longitudinal measurement invariance, ICC or within-person variance, and sensitivity analyses were reported much less consistently. Null within-person paths were common but often interpreted without sufficient attention to diagnostic sensitivity. We argue that RI-CLPM results should be interpreted conditionally on data readiness and offer practical reporting recommendations.