Accounting for Measurement Error and Untruthfulness in Binary RRT Models

Bailey Meche, Venu Poruri, Sat Gupta () and Sadia Khalil
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Bailey Meche: Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Venu Poruri: Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA
Sat Gupta: Department of Mathematics and Statistics, UNC Greensboro, Greensboro, NC 27412, USA
Sadia Khalil: Department of Mathematics and Statistics, UNC Greensboro, Greensboro, NC 27412, USA

Mathematics, 2024, vol. 12, issue 6, 1-15

Abstract: This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods.

Keywords: measurement error; untruthfulness; privacy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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