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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Chapman & Hall/Crc Statistics in the Social and Behavioral Sciences) - Tapa blanda
Sinopsis
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/.
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Acerca del autor
Guo-Liang Tian is an associate professor of statistics in the Department of Statistics and Actuarial Science at the University of Hong Kong. Dr. Tian has published more than 60 (bio)statistical and medical papers in international peer-reviewed journals on missing data analysis, constrained parameter models and variable selection, sample surveys with sensitive questions, and cancer clinical trial and design. He is also the co-author of two books. He received a PhD in statistics from the Institute of Applied Mathematics, Chinese Academy of Science.
Man-Lai Tang is an associate professor in the Department of Mathematics at Hong Kong Baptist University. Dr. Tang is an editorial board member of Advances and Applications in Statistical Sciences and the Journal of Probability and Statistics; associate editor of Communications in Statistics-Theory and Methods and Communications in Statistics-Simulation and Computation; and editorial advisory board member of the Open Medical Informatics Journal. His research interests include exact methods for discrete data, equivalence/non-inferiority trials, and biostatistics. He received a PhD in biostatistics from UCLA.
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- EditorialRoutledge
- Año de publicación2019
- ISBN 10 0367379627
- ISBN 13 9780367379629
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas324
- Contacto del fabricanteno disponible
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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys
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Taylor & Francis Ltd, 2019
ISBN 10: 0367379627
ISBN 13: 9780367379629
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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys
Publicado por
Taylor & Francis Ltd, 2019
ISBN 10: 0367379627
ISBN 13: 9780367379629
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Paperback / softback
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Incomplete Categorical Data Design
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Chapman And Hall/CRC Okt 2019, 2019
ISBN 10: 0367379627
ISBN 13: 9780367379629
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 saasweb.hku.hk/staff/gltian/. 324 pp. Englisch. Nº de ref. del artículo: 9780367379629
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Incomplete Categorical Data Design : Non-Randomized Response Techniques for Sensitive Questions in Surveys
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ISBN 13: 9780367379629
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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Chapman & Hall/Crc Statistics in the Social and Behavioral Sciences)
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Chapman and Hall/CRC, 2019
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ISBN 13: 9780367379629
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Incomplete Categorical Data Design : Non-Randomized Response Techniques for Sensitive Questions in Surveys
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ISBN 10: 0367379627
ISBN 13: 9780367379629
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Incomplete Categorical Data Design : Non-Randomized Response Techniques for Sensitive Questions in Surveys
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Chapman and Hall/CRC, 2019
ISBN 10: 0367379627
ISBN 13: 9780367379629
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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Chapman & Hall/Crc Statistics in the Social and Behavioral Sciences)
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Chapman and Hall/CRC, 2019
ISBN 10: 0367379627
ISBN 13: 9780367379629
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