Assessing treatment effects based on the two-wave latent change score model - an alternative to repeated measures ANOVA.

Behavioral science and health psychology researchers often strive to investigate treatment effects using traditional statistical approaches, such as repeated measures ANOVA. However, these methods often fall short in addressing complexities like measurement error, intraindividual variability, and change processes over time. This study introduces the Two-Wave Latent Change Score Model (2W-LCSM; Henk & Castro-Schilo, 2016) as a robust alternative for modeling treatment-induced change and its long-term behavioral consequences. We demonstrate an illustrative example using data from individuals convicted of sexual crimes, incarcerated, and completing psychotherapy programs based on cognitive behavioral therapy. Our findings highlight the utility of 2W-LCSM in capturing both within-person change and its predictive relationship with recidivism. Results indicate a significant reduction in cognitive distortions post-treatment, with latent change scores emerging as a significant predictor of reduced sexual crime recidivism. These findings underscore the value of 2W-LCSM in behavioral medicine research, offering insights for tailoring interventions and advancing statistical methodologies in the field.