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"Preface We are surrounded by missing data. Problems created by missing data in statistical analysis have long been swept under the carpet. These times are now slowly coming to an end. The array of techniques to deal with missing data has expanded considerably during the last decennia. This book is about one such method: multiple imputation. Multiple imputation is one of the great ideas in statistical science. The technique is simple, elegant and powerful. It is simple because it flls the holes in the data with plausible values. It is elegant because the uncertainty about the unknown data is coded in the data itself. And it is powerful because it can solve 'other' problems that are actually missing data problems in disguise. Over the last 20 years, I have applied multiple imputation in a wide variety of projects. I believe the time is ripe for multiple imputation to enter mainstream statistics. Computers and software are now potent enough to do the required calculations with little e ort. What is still missing is a book that explains the basic ideas, and that shows how these ideas can be put to practice. My hope is that this book can ll this gap. The text assumes familiarity with basic statistical concepts and multivariate methods. The book is intended for two audiences: - (bio)statisticians, epidemiologists and methodologists in the social and health sciences; - substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes. In writing this text, I have tried to avoid mathematical and technical details as far as possible. Formula's are accompanied by a verbal statement that explains the formula in layman terms"--

Multivariate analysis --- Multiple imputation (Statistics) --- Missing observations (Statistics) --- 51-77 --- 519.23 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Imputation, Multiple (Statistics) --- Monte Carlo method --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Mathematics for behavioural and social sciences

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This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master’s and PhD students with a sound basic knowledge of statistics. .

Statistics . --- Psychology—Methodology. --- Psychological measurement. --- Statistics for Social Sciences, Humanities, Law. --- Psychological Methods/Evaluation. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Statistics and Computing/Statistics Programs. --- Multiple imputation (Statistics) --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Imputation, Multiple (Statistics) --- Monte Carlo method --- Missing observations (Statistics) --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Measurement --- Scaling --- Methodology --- R (Computer program language).