The effects of microsuppression on state education data quality

Abstract

States often turn to a data masking procedure called microsuppression in order to reduce the risk of disclosing student records when sharing data with external researchers. This process removes records deemed to have high risk for disclosure should they be released. However, this process can lead to analyses that differ from those conducted on the complete (unmasked) data, especially if the records that are released reflect different types of students than those that are suppressed. This paper assesses the extent to which microsuppression can bias parameter estimates, and finds that while marginal test score means tend to be preserved in the masked data, conditional means for subgroups can exhibit bias as large as 0.3 standard deviations.

Jacob M. Schauer

Assistant Professor

My research interests involve statistical methods for the social and health sciences.