TY - JOUR
T1 - The role of statistical disclosure limitation in total survey error
JF - Total Survey Error in Practice
Y1 - 2017
A1 - A. F. Karr
KW - big data issues
KW - data quality
KW - data swapping
KW - decision quality
KW - risk-utility paradigms
KW - Statistical Disclosure Limitation
KW - total survey error
AB - This chapter presents the thesis, which is statistical disclosure limitation (SDL) that ought to be viewed as an integral component of total survey error (TSE). TSE and SDL will move forward together, but integrating multiple criteria: cost, risk, data quality, and decision quality. The chapter explores the value of unifying two key TSE procedures - editing and imputation - with SDL. It discusses “Big data” issues, which contains a mathematical formulation that, at least conceptually and at some point in the future, does unify TSE and SDL. Modern approaches to SDL are based explicitly or implicitly on tradeoffs between disclosure risk and data utility. There are three principal classes of SDL methods: reduction/coarsening techniques; perturbative methods; and synthetic data methods. Data swapping is among the most frequently applied SDL methods for categorical data. The chapter sketches how it can be informed by knowledge of TSE.
ER -