Case study - Analyzing suppressed data: Oregon graduation rates by gender, racial and ethnic groups

Title: Analyzing suppressed data: Oregon graduation rates by gender, racial and ethnic groups

Author: Antonio R. Vargas (@AntonioV)

Abstract: The Every Student Succeeds Act (ESSA), enacted in 2015, requires states to provide data “that can be cross-tabulated by, at a minimum, each major racial and ethnic group, gender, English proficiency status, and children with or without disabilities,”) taking care not to reveal personally identifiable information about any individual student. As state education agencies come into compliance with ESSA, they will be publishing more and more datasets which at least partially suppress or omit data to protect student privacy. In this article we will give an example of how suppressed data can be analyzed from a Bayesian perspective using cross-tabulated data on graduation rates recently released by the Oregon Department of Education.

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A nice case study on marginalizing out N in a binomial distribution when N or k is suppressed. In this case study when the cohort size (N) is smaller than 10, or that one of the groups (graduates or non-graduates) are smaller than 10.

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