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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). --- Statistics. --- Psychometrics.