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Incomplete Categorical Data Design

Non-Randomized Response Techniques for Sensitive Questions in Surveys

By Guo-Liang Tian, Man-Lai Tang

Chapman and Hall/CRC – 2013 – 319 pages

Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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    978-1-43-985533-1
    August 16th 2013

Description

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/.

Contents

Introduction

Randomized Response Models

Item Count Techniques

Non-Randomized Response Models

Scope of the Rest of the Book

The Crosswise Model

The Warner Model

A Non-Randomized Warner Model: The Crosswise Model

Bayesian Methods for the Crosswise Model

Analyzing the Induced Abortion Data

An Experimental Survey Measuring Plagiarism

The Triangular Model

The Triangular Design

Comparison with the Warner Model

Asymptotic Properties of the MLE

Bayesian Methods for the Triangular Model

Analyzing the Sexual Behavior Data

Case Studies on Premarital Sexual Behavior

Sample Sizes for the Crosswise and Triangular Models

Precision and Power Analysis Methods

The Triangular Model for One-Sample Problem

The Crosswise Model for One-Sample Problem

Comparison for the Crosswise and Triangular Models

The Triangular Model for Two-Sample Problem

An Example

The Multi-Category Triangular Model

A Brief Literature Review

The Survey Design

Likelihood-Based Inferences

Bayesian Inferences

Questionnaire on Sexual Activities in Korean Adolescents

The Hidden Sensitivity Model

Background

The Survey Design

Likelihood-Based Inferences

Information Loss and Design Consideration

Simulation Studies

Bayesian Inferences under Dirichlet Prior

Bayesian Inferences under Other Priors

Analyzing HIV Data in an AIDS Study

The Parallel Model

The Unrelated Question Model

A Non-Randomized Unrelated Question Model: The Parallel Model

Comparison with the Crosswise Model

Comparison with the Triangular Model

Bayesian Inferences

An Example: Induced Abortion in Mexico

A Case Study on College Students’ Premarital Sexual Behavior at Wuhan

A Case Study on Plagiarism at The University of Hong Kong

Discussion

Sample Size Calculation for the Parallel Model

Sample Sizes for One-Sample Problem

Comparison with the Crosswise Model

Comparison with the Triangular Model

Sample Size for Two-Sample Problem

An Example

The Multi-Category Parallel Model

The Survey Design

Likelihood-Based Inferences

Bayesian Inferences

A Special Case of the Multi-Category Parallel Model

Comparison with the Multi-Category Triangular Model

An Example

Discussion

A Variant of the Parallel Model

The Survey Design and Basic Properties

Statistical Inferences on π

Statistical Inferences on θ

Bootstrap Confidence Intervals

Bayesian Inferences

Comparison with the Crosswise Model

Comparison with the Triangular Model

The Noncompliance Behavior

An Illustrative Example of Sexual Practices

Case Studies on Cheating Behavior in Examinations

Discussion

The Combination Questionnaire Model

The Survey Design

Likelihood-Based Inferences

Bayesian Inferences

Analyzing Cervical Cancer Data in Atlanta

Group Dirichlet Distribution

Appendix A The EM and DA Algorithms

Appendix B The Exact IBF Sampling

Appendix C Some Statistical Distributions

References

Author Index

Subject Index

Author Bio

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.

Name: Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys (Hardback)Chapman and Hall/CRC 
Description: By Guo-Liang Tian, Man-Lai Tang. 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...
Categories: Quantitative Methods, Statistics for the Biological Sciences, Statistical Theory & Methods