Random Intercept CLPM (RI-CLPM)

The random intercept cross-lagged panel model (RI-CLPM) separates stable between-person differences from within-person fluctuations to estimate reciprocal lagged effects between variables measured over repeated waves.

무선절편 교차지연 패널모형(RI-CLPM)은 안정적인 개인 간 차이와 개인 내 변동을 분리하여, 반복 측정된 변수들 사이의 상호 지연효과를 추정하는 모형이다.

How do I test whether two variables influence each other over time within people?

두 변수가 시간에 걸쳐 개인 안에서 서로 영향을 주는지 어떻게 검정하나요?

The random intercept cross-lagged panel model estimates whether within-person changes in one variable predict later within-person changes in another, after removing stable between-person differences. It fits panel data with at least three waves and is preferred over the traditional cross-lagged panel model when trait-like stability would bias lagged effects.

When to use Random Intercept CLPM (RI-CLPM)

• Two or more variables measured on the same people at three or more waves
• Reciprocal or cross-lagged effects within persons are of interest
• Stable individual differences could confound lagged associations

When to use something else

• Only two waves; use a traditional cross-lagged panel model with caution
• Interest in individual growth trajectories; use latent growth curve modeling

Assumptions and requirements

• At least three measurement waves
• Approximately equal intervals between waves
• Measurement invariance across waves
• Correctly specified lag structure

What the analysis reports

• Random intercept variances and their correlation
• Autoregressive paths
• Cross-lagged paths
• Model fit indices (chi-square, CFI, RMSEA, SRMR)

How to report it

Within persons, [variable X] at wave t predicted [variable Y] at wave t+1, β = [value], p = [value], and the model fit well, CFI = [value], RMSEA = [value].

Related methods

• Cross-Lagged Panel Model
• Latent Growth Curve Model
• Panel Vector Autoregression

Run Random Intercept CLPM (RI-CLPM) on Quorant