The question of whether some interventions lead to more consistent (i.e., similar) degrees of change across treated individuals than others remains a critical overlooked question in psychology, education, and related fields. Questions of consistency are becoming increasingly important as researchers develop adaptations, such as digital or peer-led versions, of evidence-based interventions, because some interventions may be more consistent than others. Standard analytic methods that are commonly used to analyze randomized controlled trial data are not well-suited to probing questions about consistency in intervention response. This tutorial explores how multiple-groups latent curve models can be used to probe questions about intervention consistency using randomized trial data. In demonstrating this novel approach, we apply multiple-groups latent curve models to three outcome variables that showed no intervention effect in a trial of an Acceptance and Commitment Therapy intervention versus minimally-enhanced usual care for anxious cancer survivors. We found that response consistency was higher in the intervention condition on two out of three outcomes, even though average amount of change did not differ between conditions. The provided R code can be readily adapted by readers for application in their datasets. Overall, the aim of this tutorial is to describe when and how to analyze consistency in intervention response using randomized trial data.