Previous work has identified Ten Components of Effective Schools which were often associated with schools and school districts whose students were achieving above average academically. The main purpose of this study was to determine if a questionnaire-based data gathering process could capture information sufficient to test the efficacy of these Ten Components upon mathematics performance in elementary and middle schools. That is, can a short questionnaire filled out by teachers and administrators adequately capture sufficient information about such characteristics as administrative practices, curriculum alignment and professional development to test whether different degrees or quality of implementation of these practices actually makes any difference in educational outcomes at the school or school district level?
Information was obtained from 828 teachers in 104 schools located in 18 school districts across three states—California, North Carolina, and Texas. Several districts in each state and several schools within each district were selected which had large proportions of economically disadvantaged students. In addition, it was attempted to get a mix of districts which exhibited either higher than average or lower than average performance among the majority of their campuses, using a criterion described in the paper.
Correlation and linear regression analyses were used to see which of the Ten Components were associated with the more successful schools, leaving aside district influence. Using Hierarchical Linear Models (HLM) analysis, the district-level aggregates derived from the survey data were used to determine which of the components were most strongly associated with higher than predicted performance among the school districts in the sample.
Strong and consistent correlations were found between school-wide average student math performance and the degree of implementation of several of the Ten Components. The survey results were even more effective in explaining variations in the average math performance of entire school districts, even when correcting for differences in the proportion of economically disadvantaged students.
The results were weakest based upon data for North Carolina.  Reasons for this are discussed.