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 data be released. However, this process can induce differences between the original data and the data that ultimately gets used in education research. This article assesses the extent to which microsuppression can bias key statistics in state education data 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.

Keywords:

• Data disclosure
• data quality
• data privacy
• SLDS
• FERPA

Notes

1 Note that if the suppression rate is 100%, which occurs for some racial subgroups in the data, then an analysis involving those subgroups will be impossible.

Additional information

Funding

This work was supported by the Institute of Education Sciences [R305B140042, R305D140045] and National Science Foundation [DGE-1437953].