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Incomplete Categorical Data Design ─ Non-Randomized Response Techniques for Sensitive Questions in Surveys
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Incomplete Categorical Data Design ─ Non-Randomized Response Techniques for Sensitive Questions in Surveys

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"Preface Acquirement of sensitive information is often needed in a broad range of statistical applications. For instance, some behavioral, epidemiological, public health and social studies may need to solicit information on reproductive history, sexual behavior, abortion, human immunode ciency virus, acquired immune de ciency syndrome, illegal drug usage, family violence, income, child abuse, employee theft, shoplifting, social security fraud, premature sign-o s on audits, in delity, driving under in uence, having a baby outside marriage, tax evasion, and cheating in university examinations. When being directly asked these sensitive survey questions, some respondents may refuse to answer and some may even provide untruthful answers in order to protect their privacy. The problem becomes even more complicated with surveys in diverse populations because of the interaction of sensitivity and respondent diversity. It is therefore difficult to draw valid inferences from these inaccurate data that include refusal bias, response bias and perhaps both. It has long been a challenge to obtain such information while having the privacy of the respondent protected and the resulting data analyzed properly. Although there are a number of methods (see, e.g., Barton, 1958) for asking embarrassing questions in non-embarrassing ways, the rst ingenious interviewing technique to overcome the above di culties is the randomized response approach, proposed by Warner (1965), that aims to encourage truthful answers from respondents. The randomized response technique is designed to ask a sensitive question according to the outcome of a randomizing device while the interviewer is blind to the outcome"--

作者簡介

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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優惠價:90 5264
若需訂購本書,請電洽客服 02-25006600[分機130、131]。

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