Abstract

In various fields of psychology, researchers commonly investigate difference in changes between treatment and control groups by analyzing data gathered before and after interventions. The most widely used analytical methods used in such cases are the difference score model and the analysis of covariance model. However, since these models may produce conflicting outcomes, researchers often get confused when determining the most appropriate method for their studies. Therefore, this study aims to offer an in-depth examination of the theoretical and empirical difference between these models, aiming to furnish guidelines on when to use which method. Initially, we introduce and illustrate each model using an example dataset to showcase their potential divergent analytical outcomes. Subsequently, we scrutinize the debate on the use of difference scores, debunking traditional criticisms grounded in oversimplified assumptions and misunderstandings. We then delve into the implicit assumptions of both models within the framework of causal inference and, drawing upon these assumptions and findings from simulation studies, furnish recommendations for selecting the appropriate method under different participant allocation methods and analytical purposes. This study endeavors to empower researchers in making informed decisions regarding their choice of analytical methods, thereby enhancing the rigor and efficacy of their investigations.