In recent years much attention has gone to school efficiency, as determined by assessing student achievement relative to expenditures at the school district level. The present study built on prior work in school efficiency with a focus on the school campus level instead of the district level. Included in the study were 28 elementary and middle school campuses in a selected school district in Texas. The approach taken in the investigation was data envelopment analysis (DEA), which provided scores for efficiency and was intended to provide clarity on efficiency research at the campus level. Past studies using the DEA model have involved business and private institutions, but not public schools. The DEA model calculated and assigned efficiency scores for each campuses. The two variable categories used to determine campus efficiency were student demographics and resource allocation. The total enrollment numbers included the number of White, economically disadvantaged, at-risk, and limited English proficiency students. The resource allocation variables included the total expenditures in instruction, instructional related services, instructional leadership, campus leadership, and student support services. The efficiency scores paired with student achievement scores determined campus efficiency and effectiveness. An effective and efficiency framework was used to represent the data with student achievement on the y-axis and campus efficiency scores on the x-axis. I applied Pearson product moment and regression analyses using the same variables as previous studies. The Pearson product moment assessed the correlation between student demographic variables, function code variables, and campus efficiency. The Pearson product showed a weak positive relationship between the number of White students and the number of LEP students enrolled in the district. The analysis also showed moderate and strong negative relationships between efficiency and instructional leadership and student support services. The regression analysis identified the student demographic and function code variables that affected the level of efficiency of each school campus. School leadership and student support services had strong negative relationships with campus efficiency. Instructional related services had a strong positive relationship, and total enrollment and White students had weak positive relationships with efficiency. Additionally, non-White, economically disadvantaged, and at-risk students yielded weak to moderate negative relationships. The use of the DEA model allows school districts to analyze the spending patterns, specifically at the campus level. This analysis also allows districts and campuses to make meaningful decisions related to classroom instruction, instructional leadership, campus leadership, and counseling services.