Incomplete categorical data design : non-randomized response techniques for sensitive questions in surveys / Guo-Liang Tian, Department of Statistics and Actuarial Science The University of Hong Kong, Hong Kong, P. R. China, Man-Lai Tang, Department of Mathematics, Hong Kong Baptist University Kowloon Tong, Hong Kong, P. R. China Department of Mathematics and Statistics, Hang Seng Management College Shatin, New Territories, Hong Kong, P. R. China.

By: Tian, Guo-Liang [author.]Contributor(s): Tang, Man-Lai [author.]Material type: Text

TextSeries: Statistics in the social and behavioral sciences seriesPublisher: Boca Raton : CRC Press, 2014Description: 1 online resource : text file, PDFContent type: text Media type: computer Carrier type: online resourceISBN: 9781439855348 (ebook : PDF)Subject(s): Sampling (Statistics) | Surveys -- Statistical methodsGenre/Form: Electronic books.Additional physical formats: Print version:: No titleOnline resources: Click here to access online Also available in print format.

Contents:

1. Introduction -- 2. The crosswise model -- 3. The triangular model -- 4. Sample sizes for the crosswise and triangular models -- 5. The multi-category triangular model -- 6. The hidden sensitivity model -- 7. The parallel model -- 8. Sample size calculation for the parallel model -- 9. The multi-category parallel model -- 10. A variant of the parallel model -- 11. The combination questionnaire model.

Summary: "Respondents to survey questions involving sensitive information, such as sexual behavior, illegal drug usage, tax evasion, and income, may refuse to answer the questions or provide untruthful answers to protect their privacy. This creates a challenge in drawing valid inferences from potentially inaccurate data. Addressing this difficulty, non-randomized response approaches enable sample survey practitioners and applied statisticians to protect the privacy of respondents and properly analyze the gathered data.Incomplete Categorical data design : non-randomized response techniques for sensitive questions in surveys is the first book on non-randomized response designs and statistical analysis methods. The techniques covered integrate the strengths of existing approaches, including randomized response models, incomplete categorical data design, the EM algorithm, the bootstrap method, and the data augmentation algorithm. A self-contained, systematic introduction, the book shows you how to draw valid statistical inferences from survey data with sensitive characteristics. It guides you in applying the non-randomized response approach in surveys and new non-randomized response designs. All R codes for the examples are available at www.saasweb.hku.hk/staff/gltian/"-- Provided by publisher.

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Includes bibliographical references (pages 281-293) and indexes.

"Respondents to survey questions involving sensitive information, such as sexual behavior, illegal drug usage, tax evasion, and income, may refuse to answer the questions or provide untruthful answers to protect their privacy. This creates a challenge in drawing valid inferences from potentially inaccurate data. Addressing this difficulty, non-randomized response approaches enable sample survey practitioners and applied statisticians to protect the privacy of respondents and properly analyze the gathered data.Incomplete Categorical data design : non-randomized response techniques for sensitive questions in surveys is the first book on non-randomized response designs and statistical analysis methods. The techniques covered integrate the strengths of existing approaches, including randomized response models, incomplete categorical data design, the EM algorithm, the bootstrap method, and the data augmentation algorithm. A self-contained, systematic introduction, the book shows you how to draw valid statistical inferences from survey data with sensitive characteristics. It guides you in applying the non-randomized response approach in surveys and new non-randomized response designs. All R codes for the examples are available at www.saasweb.hku.hk/staff/gltian/"-- Provided by publisher.

Also available in print format.

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