Geographic Weighted Regression: New Opportunities for Data-Driven Decisions

• In Event: 69.058 - Poster Session 16
  In Poster Session: 69.058-9 - Expanding Data-Driven Decision-Making Methodology and Perspectives

Abstract

The research provided with this study examines the relationship between district-level school composition variables, literacy achievement, and the geographic variation of these relationships across the state of Missouri. In particular, this analysis makes the case that district-level aggregate data can be used in a new and different way to examine school composition relationships. More specifically, the analysis provided here demonstrates which district-level variables have a statistically significant correlation with literacy achievement. By identifying these relationships and the district locations new opportunities are created at the district level to address the persistent inequities present in literacy achievement. This technique, then, offers affordances both to the body of research as well as to inform data-driven decision making.

Author

• Elizabeth Thorne Wallington, Missouri Western State University