Missing Data
A Gentle Introduction
Mcknight, Patrick E.
Mcknight, Katherine M.
Sidani, Souraya
Figueredo, Aurelio Jose
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BOOK SUMMARY
While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the
BOOK SYNOPSIS
While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use.
AUTHOR BIO
Patrick E. McKnight, PhD, is Assistant Professor in the Department of Psychology at George Mason University, Fairfax, Virginia. The majority of his work focuses on health services outcomes and, in particular, on measuring those outcomes to make them readily interpretable. He has worked and published in the health-related areas of asthma, arthritis, cancer, speech, pain, low vision, and rehabilitation. Dr. McKnight is an active member of the American Evaluation Association, serving as co-chair of the quantitative methods topical interest group for the past 4 years. Katherine M. McKnight, PhD, teaches statistics at George Mason University, Fairfax, Virginia, and is Director of Evaluation for LessonLab Research Institute, part of Pearson Achievement Solutions. She has published numerous articles reflecting a wide range of interests, with the common underlying framework of the thoughtful use of research methods, measurement, and data analysis for addressing research and evaluation questions. She is a member of the American Evaluation Association and the Association for Psychological Science. Souraya Sidani, PhD, RN, is Professor in the Faculty of Nursing, University of Toronto. Her areas of expertise are in quantitative research methods, intervention design and evaluation, and measurement. Her research areas of interest focus on evaluating interventions and on refining research methods and measures for determining the clinical effectiveness of interventions. She is a member of the American Evaluation Society and the Canadian Evaluation Society. Aurelio José Figueredo, PhD, is Professor of Psychology at the University of Arizona. He is the director of the graduate program in Ethology and Evolutionary Psychology (EEP), a cross-disciplinary program integrating the studies of comparative psychology, ethology, sociobiology, and behavioral ecology, genetics, and development. His major areas of research interest are the evolutionary psychology and behavioral development of life-history strategy and sex and violence in human and nonhuman animals, and the quantitative ethology and social development of insects, birds, and primates. In the EEP he regularly teaches the graduate year-long course in Statistical Methods in Psychological Research.
BOOK REVIEWS
"This book is full of useful information about methodological and statistical issues related to missing data. It includes clear definitions of types of missing data, ways to reduce their negative effects, and analytical strategies for maximizing the use of all data--even partial data--collected in a research study. A unique strength of the book is its focus on missing data as a threat to the validity of a study's conclusions. Unlike other sources on missing data analysis, design approaches for preventing missing data are emphasized. More advanced statistical approaches to missing data analysis are also described clearly. This is a valuable, practical resource."--David MacKinnon, Department of Psychology, Arizona State University "This very important, interesting, and well-written book addresses a serious problem in contemporary social science research. Statisticians have made considerable progress in developing methodologies for dealing with missing data. However, these methods are not well known to social science researchers or to many graduate students in the behavioral sciences. This book systematically explores methods for classification, diagnosis, and prevention of missing data problems. It provides step-by-step instructions for analyzing data sets with some observations missing; reviews imputation methods; and advises investigators on how to report on analyses when some participants have been lost to follow-up. This is an excellent book that will help behavioral science investigators handle analytical problems for virtually every study they conduct."--Robert M. Kaplan, Department of Health Services, UCLA School of Public Health
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MORE BOOK INFO
ISBN: 1593853939
ISBN(13-digit): 9781593853938
Dewey Decimal: 300.72
Library of Congress: 2007001824
Book Publisher: Guilford Pubn
Language: ENG
No. of Pages: 251
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