Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

Designing and Conducting Health Surveys is written for students, teachers, researchers, and anyone who conducts health surveys. This third edition of the standard reference in the field draws heavily on the most recent methodological research on survey design and the rich storehouse of insights and implications provided by cognitive research on question and questionnaire design in particular. This important resource presents a total survey error framework that is a useful compass for charting the dangerous waters between systematic and random errors that inevitably accompany the survey design enterprise. In addition, three new studies based on national, international, and state and local surveys—the UNICEF Multiple Indicator Cluster Surveys, California Health Interview Survey, and National Dental Malpractice Survey—are detailed that illustrate the range of design alternatives available at each stage of developing a survey and provide a sound basis for choosing among them.

This new edition attempts to provide a broad picture of cardiovascular disease epidemiology including survey methods, experimental methods, and new methods appropriate for use in developed and developing countries. It also goes beyond practical guidelines to provide detailed methods useful in the field for data collection, editing, analysis, and interpretation. The book is not only a manual of operations for surveys but provides, as well, the conceptual background and literature base for the research approaches and procedures that it proposes. A complete source and critical reference for the many and varied health care professionals and support personnel involved in cardiovascular research in evaluation of health care effects and costs in hospital, and population surveillance of trends, and in treatment and prevention trials of new agents instruments and procedures. A compendium of methods and forms on a computer disk is included.

Survey research is one of the most widely used research methodologies across the social and behavioral sciences. Two trends that have had a major impact on the development of survey methods over the last decade are (1) the application of techniques and theories from cognitive psychology to the understanding and reduction of survey measurement error, and (2) the application of new computer and telephony technologies to data collection and analysis. These trends and other emerging issues from the 1990's literature on survey research methods are captured here in 617 detailed annotations to monographs, journals, government documents, dissertations, and ERIC documents. Annotations include examples from business, criminology, education, health and medicine, law, library science, mass media, military science, political science, psychology, sociology, social work, religion, and women's studies. The bibliographic entries provide every useful element, including series names, complete subtitles, and overall text page numbers as well as chapter page numbers. The extensive annotations are more complete, and more detailed than is typical for annotated bibliographies. The descriptions include highlights of the study data and sufficient detail to enable the reader to make an informed choice as to whether to seek the full text. Appendices include journals cited and the major survey research organizations. The annotations are easily accessed through author and subject indexes.