This project aims to show, through the use of rigorous longitudinal statistics, what factors influence teacher and student success in grade schools. The data are gathered through comparison of demographically and academically matched schools, and includes test scores and surveys. Industry standard data preparation algorithms will be coded and performed on the data in the programing language R including comprehensive variable coding, correcting formatting errors, organizational management, automated data cleaning, and data melting and recasting. Following, exploratory analysis will include automated statistical analysis for significant differences between years, distribution plots, and other exploratory analysis. Preliminary results indicate that there are factors, such as self-efficacy, which yield a significant difference in teacher and student performance.