Questions to Ask


    Do schools collect and report data on performance of all groups within each school, particularly economically disadvantaged students and English-language learners?


    Are there methods for determining the margin of error associated with disaggregated data?


Comprehensiveness. Breaking out test results by race, gender, income, and other categories enhances the quality of the data and provides a more complete picture of achievement in a school or district.

Accuracy. In order to enhance the quality of inferences about achievement drawn from the data, states and districts need to reveal the extent of error and demonstrate how that error affects the results.

Privacy. When groups of students are so small that there is a risk of violating their privacy, the results for these groups should not be reported.


The following example describes the practice in a state that disaggregates test data for each school and uses the disaggregated data to hold schools accountable for performance.

Under the Texas accountability system, the state rates districts each year in four categories—exemplary, recognized, academically acceptable, and academically unacceptable—and rates schools as exemplary, recognized, acceptable, and low-performing. The ratings are based on student performance on the state test, the Texas Assessment of Academic Skills, the dropout rate, and the attendance rate. In order to earn a coveted “exemplary” or “recognized” rating, districts or schools must not only have a high overall passing rate on the TAAS, a low overall dropout rate, and a high overall attendance rate, but the rates for each group within a school or district—African Americans, Hispanics, whites, and economically disadvantaged students under the state's designations—must also exceed the standard for each category. Schools that might have met the requirements for a high rating because of high average performance but fell short because of relatively low performance by students from a particular group have focused their efforts on improving the lagging group's performance—a response that might not have taken place if they had not disaggregated the results.

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