Impact of Partial Randomized Response Technique on Stratified Calibration Estimators with Different Types of Distance Measures in the Presence of Measurement Error

Calibration is a methodological approach that enhances the accuracy of population parameter estimates through the integration of auxiliary information. In this study, a new ratio calibration method has been developed with various distance measures under the Partial Randomized Response Technique (PRRT) in the presence of measurement error to address the problem of estimating the population mean under stratified sampling. Additionally, to show the impact of measurement error under the PRRT model, the proposed calibration estimators are compared with similar direct estimators (i.e., in the absence of measurement error and randomization). The effectiveness of the proposed calibration estimators is evaluated through a simulation study using a real data set.