Incomplete Categorical Data Design
Non-Randomized Response Techniques for Sensitive Questions in Surveys
Bibliographic Data
| ID | 6576596 |
|---|---|
| Authors | Guo-Liang Tian, Guo‐Liang Tian (0000-0003-3749-6188), Man-Lai Tang, Man‐Lai Tang (0000-0001-6460-8136) |
| Year | 2016 |
| Publication date | 2016-01-01 |
| Open Access | No |
| Type | BOOK |
| Venue | Incomplete Categorical Data Design (SOURCE_BOOK) |
| Publisher | Taylor & Francis Group (PUBLISHER) |
| DOI | 10.1201/b15355 |
| OpenAlex | W4253663741 |
| ISBN | 9781439855348 |
| Open Library | OL34606908M |
| Language | EN |
| Citations received | 1 |
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-rando
Categorical variable · Machine learning · Sampling (Statistics · Social surveys · Advanced Statistical Methods and Models · Computer Science · Sampling (Statistics) · Artificial Intelligence
| Citations per year | 0.14 |
|---|---|
| Citation span | 2019 - 2019 (1) |
| Highly cited | No |