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/.
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