Tian / Tang Incomplete Categorical Data Design

Non-Randomized Response Techniques for Sensitive Questions in Surveys
Erscheinungsjahr 2013
ISBN: 978-1-4398-5534-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 319 Seiten

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/.

Zielgruppe

Applied statisticians in the social and behavioral sciences; biostatisticians.

Autoren/Hrsg.

Tian, Guo-Liang

Tang, Man-Lai

Fachgebiete

• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

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
Com

Über Autor(innen)

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.