What does this research mean for the field?

Calibration-based estimators that integrate Compulsory and Optional Randomized Response Techniques can effectively estimate sensitive population means in a two-occasion successive sampling framework while explicitly accounting for measurement error. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate calibration-based estimators for sensitive population means in a two-occasion successive sampling framework that explicitly accounts for measurement error.

September 3, 2026Journal of Statistical Theory and PracticeOpen Access

Analyzing Measurement Error in Sensitive Mean Estimation: A Two-Occasion Successive Sampling Approach

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To develop and evaluate calibration-based estimators for sensitive population means in a two-occasion successive sampling framework that explicitly accounts for measurement error.
Integrated Compulsory and Optional Randomized Response Techniques across two successive sampling occasions.
Formulated calibration estimators accounting for measurement error and compared their theoretical efficiency against direct estimators.
Conducted a simulation study using natural population data on COVID-19 infection to assess practical estimator performance.
Proposed calibration estimators exhibited greater estimation accuracy and efficiency compared to standard direct estimators when measurement errors were present.
The combination of randomized response techniques across both sampling waves effectively mitigated respondent bias while preserving sensitive survey information.

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Implication

Simulation study demonstrates improved sensitive mean estimation under measurement error using calibration in successive sampling, indicating higher survey accuracy for sensitive public health data.

Cite This Study

Trisandhya et al. (2026) studied this question.

synapsesocial.com/papers/6a9934b4636c6408cfa7c668 https://doi.org/10.1007/s42519-026-00636-6

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Implication

Simulation study demonstrates improved sensitive mean estimation under measurement error using calibration in successive sampling, indicating higher survey accuracy for sensitive public health data.

Analyzing Measurement Error in Sensitive Mean Estimation: A Two-Occasion Successive Sampling Approach

Key Points

Discussion

Implication

Cite This Study

Analyzing Measurement Error in Sensitive Mean Estimation: A Two-Occasion Successive Sampling Approach

Key Points

Discussion

Implication

Cite This Study

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