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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys Guo-Liang Tian 第1 版
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Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys
Guo-Liang Tian
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/.
322 pages
| メディア | 書籍 Paperback Book (ソフトカバーで背表紙を接着した本) |
| リリース済み | 2019年10月7日 |
| ISBN13 | 9780367379629 |
| 出版社 | Taylor & Francis Ltd |
| ページ数 | 322 |
| 寸法 | 150 × 220 × 10 mm · 508 g |
| 言語 | 英語 |
