Kristian Kleinke

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.

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.

Strukturgleichungsmodelle eignen sich hervorragend f r die empirische Analyse zahlreicher Fragestellungen und stellen f r die Sozial- und Wirtschaftswissenschaften eine unverzichtbare statistische Methode dar. Mit Mplus steht ein besonders flexibles und vergleichsweise anwenderfreundliches Statistikprogramm f r die Strukturgleichungsmodellierung zur Verf gung. Ziel dieses Lehrbuchs ist es, den Leserinnen und Lesern ein umfassendes Verst ndnis der Durchf hrung grundlegender und weiterf hrender Anwendungen von Strukturgleichungsmodellen in Mplus zu vermitteln. Zu den in diesem Buch behandelten Verfahren z hlen: explorative und konfi rmatorische Faktorenanalysen, einfache Strukturgleichungsmodelle, multiple Gruppenvergleiche, autoregressive Modelle, Wachstumskurvenmodelle und Mehrebenen-Strukturgleichungsmodelle.Neu in der zweiten Aufl age ist das Thema Moderation und Sch tzung konditionaler indirekter Effekte. Ferner wird auf den Mplus-Diagrammer und die mit Mplus Version 7.1 eingef hrten Optionen zur Pr fung von Messinvarianz eingegangen.