This study identified the variables associated with data-driven instruction (DDI) that are perceived to best predict student achievement. Of the DDI variables discussed in the literature, 51 of them had a sufficient enough research base to warrant statistical analysis. Of them, 26 were statistically significant. Multiple regression and an iterative process of coding and recoding open-ended responses were used to identify perceived achievement predictors. Among the most important achievement predictor variables were working in grade- or content-alike teams, understanding and using DDI as a tool for closing achievement gaps, and having school leadership provide a clear vision for data use. In addition to achievement, a number of variables were perceived to predict equity, educator effectiveness, and educator efficacy. Open-ended responses revealed that DDI was perceived as a critical aid to future instruction and as a powerful tool for feedback. On the other hand, it was also perceived as time-consuming and as provoking a narrow focus on test scores. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]