Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Chapman & Hall/Crc Statistics in the Social and Behavioral Sciences)

Tian, Guo-Liang; Tang, Man-Lai

ISBN 10: 0367379627 / ISBN 13: 9780367379629

Verlag: Chapman and Hall/CRC, 2019

Beschreibung

Bestandsnummer des Verkäufers 18388456709

Bibliografische Details

• Titel: Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Chapman & Hall/Crc Statistics in the Social and Behavioral Sciences)
• Verlag: Chapman and Hall/CRC
• Veröffentlichungsdatum: 2019
• Sprache: Englisch
• ISBN-10: 0367379627
• ISBN-13: 9780367379629
• Bindung: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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/.

Über die Autorin bzw. den 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.