The Effects of Microsuppression on State Education Data Quality

Jacob M. Schauer*, Arend M Kuyper, Eric C Hedberg, Larry V. Hedges

*Corresponding author for this work

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Research on Educational Effectiveness
DOIs
StateAccepted/In press - 2020

Keywords

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

ASJC Scopus subject areas

  • Education

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