School Performance and Funding Analysis
School Performance and Funding Analysis
Research Websites and Files
(1) First, process all uploaded '6Book1.xls' files (Report.csv, Sheet1.csv, Sheet2.csv, Sheet3.csv, Non Blans.csv) to create a comprehensive school performance dataset, and process 'ESSA_PerPupil.xls - ESSA_PerPupil.csv' for financial data. Use 'District Code'/'DIST' and 'School Code'/'LOC' as common keys.
(2) Analyze the '6Book1.xls' files to determine the overall average 'Assessment ELA Performance Value' and 'Assessment Math Performance Value' for the 'Total' subgroup across all schools. Calculate the percentage of schools that are meeting their annual targets for both ELA and Math.
(3) Using the '6Book1.xls' files, conduct a detailed analysis of performance gaps. Calculate the average ELA and Math performance difference between the 'Economically Disadvantaged' subgroup and the 'Total' subgroup, and also between the 'Children With Disabilities' subgroup and the 'Total' subgroup.
(4) Compare the average ELA and Math performance values from the '6Book1.xls' files across the different 'School Type' categories (Elementary, Middle, Secondary) to identify which school type performs best and worst on average.
(5) Based on the 'ESSA_PerPupil.xls - ESSA_PerPupil.csv' file, identify the districts and schools with the highest and lowest 'GR_TOT' (Grand Total Per Pupil Expenditure). Compare the average 'STPERPUP' (State Per Pupil Expenditure) versus 'FEDPERPUP' (Federal Per Pupil Expenditure) across all districts.
(6) Synthesize data from all files to investigate the relationship between funding and outcomes. Correlate the 'GR_TOT' (Grand Total Per Pupil Expenditure) from the 'ESSA_PerPupil.csv' file with the 'Assessment ELA Performance Value' and 'Assessment Math Performance Value' from the '6Book1.xls' files (for the 'Total' subgroup). Identify any outlier schools (e.g., high performance with low spending, or low performance with high spending).
(7) Analyze the 'Title 1 Indicator' from the '6Book1.xls' files. Compare the average 'GR_TOT' and 'FEDPERPUP' (from 'ESSA_PerPupil.csv') for Title 1 schools versus non-Title 1 schools. Then, compare the average ELA and Math performance for these two groups.
(8) Isolate all data for the 'Mercer' district (District Code 51) from all files. Provide specific insights on its performance (ELA/Math values and target achievement) compared to the state average (from 'State Record' row, if available) and its per-pupil expenditure ('GR_TOT') compared to other districts like 'Barbour' (District 2) and 'Berkeley' (District 4).
Research Websites and Files
(1) First, process all uploaded '6Book1.xls' files (Report.csv, Sheet1.csv, Sheet2.csv, Sheet3.csv, Non Blans.csv) to create a comprehensive school performance dataset, and process 'ESSA_PerPupil.xls - ESSA_PerPupil.csv' for financial data. Use 'District Code'/'DIST' and 'School Code'/'LOC' as common keys.
(2) Analyze the '6Book1.xls' files to determine the overall average 'Assessment ELA Performance Value' and 'Assessment Math Performance Value' for the 'Total' subgroup across all schools. Calculate the percentage of schools that are meeting their annual targets for both ELA and Math.
(3) Using the '6Book1.xls' files, conduct a detailed analysis of performance gaps. Calculate the average ELA and Math performance difference between the 'Economically Disadvantaged' subgroup and the 'Total' subgroup, and also between the 'Children With Disabilities' subgroup and the 'Total' subgroup.
(4) Compare the average ELA and Math performance values from the '6Book1.xls' files across the different 'School Type' categories (Elementary, Middle, Secondary) to identify which school type performs best and worst on average.
(5) Based on the 'ESSA_PerPupil.xls - ESSA_PerPupil.csv' file, identify the districts and schools with the highest and lowest 'GR_TOT' (Grand Total Per Pupil Expenditure). Compare the average 'STPERPUP' (State Per Pupil Expenditure) versus 'FEDPERPUP' (Federal Per Pupil Expenditure) across all districts.
(6) Synthesize data from all files to investigate the relationship between funding and outcomes. Correlate the 'GR_TOT' (Grand Total Per Pupil Expenditure) from the 'ESSA_PerPupil.csv' file with the 'Assessment ELA Performance Value' and 'Assessment Math Performance Value' from the '6Book1.xls' files (for the 'Total' subgroup). Identify any outlier schools (e.g., high performance with low spending, or low performance with high spending).
(7) Analyze the 'Title 1 Indicator' from the '6Book1.xls' files. Compare the average 'GR_TOT' and 'FEDPERPUP' (from 'ESSA_PerPupil.csv') for Title 1 schools versus non-Title 1 schools. Then, compare the average ELA and Math performance for these two groups.
(8) Isolate all data for the 'Mercer' district (District Code 51) from all files. Provide specific insights on its performance (ELA/Math values and target achievement) compared to the state average (from 'State Record' row, if available) and its per-pupil expenditure ('GR_TOT') compared to other districts like 'Barbour' (District 2) and 'Berkeley' (District 4).
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