The Every Student Succeeds Act (ESSA) requires states to identify schools with low-performing student subgroups for Targeted Support and Improvement (TSI) or Additional Targeted Support and Improvement (ATSI). Random differences between students’ true abilities and their test scores, also called measurement error, reduce the statistical reliability of the performance measures used to identify schools for these categorizations. Measurement error introduces a risk that the identified schools are unlucky rather than truly low performing. Using data provided by the Pennsylvania Department of Education, the study team used Bayesian hierarchical modeling to improve the reliability of subgroup proficiency measures and demonstrate the approach’s efficacy.

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