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Randomized Response and Related Methods
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Randomized Response and Related Methods
Surveying Sensitive Data

Second Edition
Volume: 58
October 2015 | 144 pages | SAGE Publications, Inc
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Randomized response is a data collection strategy specifically designed for surveys of a sensitive nature. By establishing a probabilistic connection between question and answer, randomized response and related methods protect respondents who are asked to disclose personal information. Covering a half century of theoretical and applied research, the Second Edition significantly updates and expands what was, at the time, the first comprehensive and practical guide to randomized response. 
 
1. Surveying Sensitive Topics
Asking Sensitive Questions

 
Types of Errors in Surveys

 
Ethical Issues in Surveys

 
Legal Means of Privacy Protections

 
Privacy Protection Through Anonymity

 
Confidentiality and Privacy Protection

 
Need for an Alternative Strategy

 
 
2. The Randomized Response Technique
Warner's Randomized Response Design

 
Unrelated Question Approach

 
Forced Alternative Response

 
Repeat Randomization

 
 
3. Design Considerations in Randomized Response
Alternative Question Content

 
Randomizing Devise and Question Selection Probability

 
Distribution of Alternative Questions

 
Reconciling Respondent Hazards and Efficiency

 
 
4. Alternatives to Randomized Response
Non-Randomized Response

 
Use of Lists and Counts

 
 
5. Extensions of Randomized Response
Generalization to Polychotomous Measures

 
Untruthful Respondents as a Special Group

 
Extension to Quantitative Measures

 
 
6. Analyzing Randomized Response Data
Measures of Association

 
Randomized Response as Disclosure Control

 
An Alternative Formulation

 
Randomized Response Software

 
 
7. Applications of Randomized Response Methods
Substantive Applications

 
Comparison Studies

 
Validation Research

 
 
8. Epilogue

Randomized Reponse and Related Methods is a comprehensive how-to guide for any researcher interested in designing, implementing, and analyzing data from an RRT.

Allyson Holbrook
University of Illinois at Chicago

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