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A neutral comparative analysis of additive, multiplicative, and mixed quantitative randomized response models.
1Department of Statistics, University of Malakand, Lower Dir, KP, Pakistan.
Plos One
|April 26, 2023
Summary
Choosing the right randomized response technique is crucial for reliable survey data. This study offers a neutral comparison of six quantitative models, considering both privacy and efficiency to guide practitioners.
Area of Science:
- Survey methodology
- Quantitative social sciences
- Statistical modeling
Background:
- Randomized response techniques (RRTs) are vital for collecting sensitive data in sociology, economics, and psychology.
- Numerous quantitative RRT models exist, but a lack of neutral comparative studies hinders practitioner selection.
- Existing research often presents biased comparisons, favoring specific models and potentially misguiding users.
Purpose of the Study:
- To provide a neutral, comparative analysis of six established quantitative randomized response models.
- To evaluate models based on both respondent privacy and model efficiency metrics.
- To assist practitioners in selecting the most appropriate RRT for specific practical applications.
Main Methods:
- Comparative analysis of six distinct quantitative randomized response models.
- Assessment using separate and joint measures of respondent privacy.
- Evaluation of model efficiency across different scenarios.
Main Results:
- No single model consistently outperforms others across all metrics (privacy and efficiency).
- A model excelling in efficiency may be suboptimal when considering privacy safeguards.
- Trade-offs between privacy and efficiency vary significantly among the evaluated models.
Conclusions:
- Practitioners must carefully consider the balance between privacy and efficiency when selecting an RRT.
- The choice of RRT model should be tailored to the specific problem and situational context.
- This study provides essential guidance for informed decision-making in survey sampling with sensitive data.
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