OKHS Field Studies (SC)

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Statistical Analysis

Building Assessment

Not applicable to the Size of the Classroom.

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©Salary.S. (2017) OKHS Research Project. The University of Oklahoma. s.salary@ou.edu


Teacher's Survey Results

In 185 completed teacher surveys, the importance of "Classrooms are large enough to meet all instructional needs" was ranked fairly high at 13 out of 92, indicating this is more important to teachers than other school factors.

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©Salary.S. (2017) OKHS Research Project. The University of Oklahoma. s.salary@ou.edu


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Also, The importance of "The library/Resources/Media Center provides adequate space" was ranked fairly high at 24 out of 92, indicating this is more important to teachers than other school factors.

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The access link to the complete list of teacher's survey and their ranking is; Teacher's Opinion


Parents' Survey Results

In a survey of 6% of parents whose children attend school in one of the two districts, the importance of "Classrooms are large enough to meet all my child's needs" was ranked fairly high at 26 out of 66, indicating this is more important to parents than other school factors.

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©Salary.S. (2017) OKHS Research Project. The University of Oklahoma. s.salary@ou.edu

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Also, The importance of "The school library and media center at my child's school has adequate space" was ranked fairly high at 19 out of 66, indicating this is more important to parents than other school factors.


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and, "Classrooms are large enough to meet all my child's teacher's needs" was ranked 31 out of 66, indicating this is important to parents compare to other school factors.


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The access link to the complete list of teacher's survey and their ranking is; Parent's Opinion

Analysis Method for Building Assessment

We evaluated the association between architectural features in classrooms and schools and school performance. We obtained data on the features of lighting, acoustics, thermal comfort, interior design, aesthetics, and school campus by conducting physical assessments of several typical classrooms per school in two metropolitan school districts. School performance data were obtained from the State’s 2015 school report card. We used the Performance Index, which scored all schools on overall school performance with a score of 0-100 and has been standardized across all schools in the state. We used multivariable linear regression to compare the relationship between one randomly selected classroom per school and overall school performance adjusted for school-level poverty. We also accounted for clustering of schools within the two districts in our analysis. This statistical method requires the data to be independent, so we randomly selected one classroom per school to include in the analysis. By including all measured classrooms in our analysis, we would have used the statistical method inappropriately. Through our analysis, we observed that classrooms differed in relation to school performance, so we ran our analysis a second time selecting a different random classroom and compared results with our original analysis. Only the results which were consistent with both analyses were included in the tool. We excluded rooms that functioned only as gyms, safe rooms, or other non-educational rooms from the analysis. We used SAS v. 9.4 for all analyses and an alpha of 0.05 to determine statistical significance. A strength of this analysis was having detailed physical assessments of classrooms in each school and in both districts. However, a major limitation was that we did not have performance data on individual students or classrooms, but only in schools. Thus, we were unable to include all classrooms in our analysis and randomly selected a classroom for each school to include. Additionally, because of variability in the classrooms, results varied depending on the classroom chosen. Due to these limitations, our results should be interpreted cautiously and are included in this tool described as either supporting or not supporting existing literature.