Incomplete categorical data design : non-randomized response techniques for sensitive questions in surveys / Guo-Liang Tian and Man-Lai Tang.

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

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 Calculationfor the Parallel Model --
9. The Multi-category Parallel Model --
10. A Variant of the Parallel Model --
11. The Combination Questionnaire Model --

Appendix A: The EM and DA Algorithms --
Appendix B: The Exact IBF Sampling--
Appendix B: Some statistical distributions--
List of figures--
List of tables--
References--
Author index--
Subject index.

"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/"--

"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"--