I am responsible for the data cleaning and statistical analysis of the entire project.
The goal of this study is to discover the underlying factors influencing school districts’ performance in Texas by using cross-sectional regression analysis. We used the data from the District and Chartered Detailed Education dataset, which contains data from 1,044 public school districts in Texas in 2005 and 2014. Multiple linear regressions are performed for data in 2005 and 2014 for possible comparison. Our analysis suggests that some of the most important predictors of school performance are teacher salary, revenue per pupil, and average daily attendance. As teacher salary and attendance increase, so does school performance. As revenue per pupil increases, school performance decreases. While teacher salary and revenue per pupil have complicated causal pathways towards school performance, attendance does not. It seems strikingly clear that one of the best ways to improve school performance–and thus student achievement–is to make sure that kids can actually come to school. When we combine this with the fact that the effect that percent economically disadvantaged students has on school performance has increased, we begin to see a clear picture of need in Texas schools. Based on our analysis, we suggest that policies supporting economically disadvantaged students and making sure that they can come to school every day will improve school performance.
Keywords: school district performance, Texas education, multiple linear regression
Download full paper here.