Accounting for Measurement Error and Untruthfulness in Binary RRT Models

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 account...

Full description

Saved in:
Bibliographic Details
Published inMathematics (Basel) Vol. 12; no. 6; p. 875
Main Authors Meche, Bailey, Poruri, Venu, Gupta, Sat, Khalil, Sadia
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2024
Subjects
Confidentiality
Efficiency
Error analysis
Measurement
measurement error
Polls & surveys
Privacy
Probability
Surveys
untruthfulness
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12060875