The DOI matches its Crossref record; 0 of the 5 references it cites were found in a registry or an index.
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Abstract
For various reasons individuals in a sample survey may prefer not to confide to the interviewer the correct answers to certain questions. In such cases the individuals may elect not to reply at all or to reply with incorrect answers. The resulting evasive answer bias is ordinarily difficult to assess. In this paper it is argued that such bias is potentially removable through allowing the interviewee to maintain privacy through the device of randomizing his response. A randomized response method for estimating a population proportion is presented as an example. Unbiased maximum likelihood estimates are obtained and their mean square errors are compared with the mean square errors of conventional estimates under various assumptions about the underlying population.
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“Existing defenses against the targeted backdoor attacks can be categorized into two types: detection methods and erasing methods [150].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [150]
Citations
Cites
Sampling Techniques, Second Edition
G., C. W. (1963)
UnresolvedSome Theory of Sampling
E., D. W. (1950)
UnresolvedSample Survey Methods and Theory, Volume I
H., H. M. (1953)
UnresolvedSample Survey Methods and Theory, Volume II
H., H. M. (1953)
UnresolvedSampling Opinions
F., S. F. (1963)
Unresolved
Flagged first, then by citations
5 of the 5 references couldn’t be matched to a known paper.
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Liu, K., Kargupta, H., & Ryan, J. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.14 · Cited by 558
Social desirability bias in voter turnout reports: Tests using the item count technique
Holbrook, A. L., & Krosnick, J. A. (2010) · Public Opinion Quarterly · doi:10.1093/poq/nfp065 · Cited by 545
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548
Differential Privacy for Deep and Federated Learning: A Survey
Ouadrhiri, A. E., & Abdelhadi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3151670 · Cited by 445
A Statistical Framework for Differential Privacy
Wasserman, L., & Zhou, S. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08651 · Cited by 438
Metadata
- Authors
- Warner, S. L.
- Affiliations
- Claremont Graduate University (US)
- Corresponding author
- Warner, Stanley L.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1965-03
- Volume
- 60
- Issue
- 309
- Pages
- 63-69
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2013823004
- Cited by
- 3,263
- References
- 5
- Citations by year
- 2026: 135 · 2025: 189 · 2024: 224 · 2023: 211 · 2022: 190 · 2021: 219 · 2020: 208 · 2019: 185 · 2018: 171 · 2017: 141 · 2016: 151 · 2015: 123 · 2014: 114 · 2013: 120 · 2012: 96
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Census and Population Estimation
- Keywords
- randomized response, sensitive questions, population proportion estimation, maximum likelihood estimation, mean squared error comparison, survey nonresponse
- MeSH terms
- Data Collection, Surveys and Questionnaires, Research, Sampling Studies
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