The article critiques the Cross-Lagged Panel Model (CLPM) and proposes an alternative model, the Random Intercepts Cross-Lagged Panel Model (RI-CLPM), to address the limitations of the CLPM. The CLPM, widely used to study causal influences in longitudinal data, assumes that constructs have temporal stability, but it fails to account for trait-like, time-invariant stability. This can lead to biased estimates of cross-lagged regression coefficients, resulting in erroneous conclusions about the presence, predominance, and sign of causal influences. The RI-CLPM includes random intercepts to separate within-person dynamics from between-person differences, providing a more accurate representation of reciprocal processes. The authors derive the analytical relationship between the cross-lagged parameters of the CLPM and the RI-CLPM and use simulations to demonstrate the spurious results that can arise when using the CLPM with data that include stable, trait-like individual differences. They also present a modeling strategy to avoid these pitfalls and illustrate it using empirical data. The implications for existing and future cross-lagged panel research are discussed, emphasizing the importance of considering alternative SEM approaches to better understand reciprocal influences over time.The article critiques the Cross-Lagged Panel Model (CLPM) and proposes an alternative model, the Random Intercepts Cross-Lagged Panel Model (RI-CLPM), to address the limitations of the CLPM. The CLPM, widely used to study causal influences in longitudinal data, assumes that constructs have temporal stability, but it fails to account for trait-like, time-invariant stability. This can lead to biased estimates of cross-lagged regression coefficients, resulting in erroneous conclusions about the presence, predominance, and sign of causal influences. The RI-CLPM includes random intercepts to separate within-person dynamics from between-person differences, providing a more accurate representation of reciprocal processes. The authors derive the analytical relationship between the cross-lagged parameters of the CLPM and the RI-CLPM and use simulations to demonstrate the spurious results that can arise when using the CLPM with data that include stable, trait-like individual differences. They also present a modeling strategy to avoid these pitfalls and illustrate it using empirical data. The implications for existing and future cross-lagged panel research are discussed, emphasizing the importance of considering alternative SEM approaches to better understand reciprocal influences over time.