Bayesian analysts use informed priors to improve analytic precision and prediction; however, rarely have they applied a mixed methods approach that uses qualitative data to develop these priors. Yet, using qualitatively informed priors can be useful when making predictions in the context of small sample sizes, which is common in school-based settings. On this basis, we advance a decision-making framework that uses Bayesian estimation and qualitative priors to assess if a given student is failing to make progress in a Response to Intervention (RTI) framework. This framework might help decision makers when there is a difficult call (e.g., there is disparate information) about whether to intensify or reduce services (i.e., move students across RTI tiers), or even change an intervention. We describe this decision-making framework by demonstrating how qualitative data can be integrated into a Bayesian analysis approach, which allows all available data (quantitative or qualitative) to support an instructional or diagnostic decision. Use of this framework might also support decisions about whether to place students into special education in the first place and thus help school staff use resources in an effective manner.