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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction and Basic Concepts

Abstract
The purpose of this chapter is to characterize and describe missing data situations, to introduce terminology used in this field, e.g., the terms item nonresponse, unit nonresponse and attrition, and review the most common statistical approaches to inference from samples to populations. Missing data patterns (univariate, monotone, arbitrary) are described as far as they are relevant for choosing an appropriate technique to compensate for missing data. Since the scope of the method of multiple imputation is rather general, those aspects of the most common statistical approaches relevant to understanding when and how multiple imputation works are reviewed. The different statistical approaches and the potential usefulness of methods to compensate for missing data are demonstrated through simple examples and illustrations.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Chapter 2. Missing Data Mechanism and Ignorability

Abstract
In this chapter the concept of a missing data mechanism and the classification of missing data as being missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR) is introduced. Consequences of different possible missing data mechanisms are considered and illustrated with simple examples. In addition, conditions are given for analysing data sets without the need to explicitly model the missing data mechanism (“ignorability”). We also review diagnostic tools for incomplete data sets, both descriptive and based on a statistical test.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Chapter 3. Missing Data Methods

Abstract
In this chapter missing data procedures and techniques are reviewed and discussed. Among them are both, ad-hoc methods but also more sophisticated techniques including maximum likelihood estimation, weighting and imputation. We discuss pros and cons of the different approaches and techniques, and give practical advice which procedure might be suited best in a given scenario because valid inferences in applied research can only be expected based on informed decisions. A conclusion of this chapter will be that there is not the one method or technique that works best under every possible scenario.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Chapter 4. Multiple Imputation: Theory

Abstract
In this chapter, we provide an overview over the theory of multiple imputation. Topics covered are justification of multiple imputation strategies based on monotone and non-monotone missing patterns, the approach based on joint modeling of all variables with missing values and the fully conditional modeling approach, where only univariate marginal models are used to generate imputations. Additional topics are rounding, how to deal with restrictions and how to treat interaction or higher polynomial terms.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Chapter 5. Multiple Imputation: Application

Abstract
In this chapter, we discuss the most important and most commonly used multiple imputation tools in R (Table 5.1 gives an overview of the download frequencies of various MI packages in R.) for both multivariate and clustered data sets, including packages mice, norm2, Amelia, mi, pan, as well as function aregImpute( ) from package Hmisc, and show practical applications. We give hands-on step by step tutorials regarding how to carry out MI in practice.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Chapter 6. Multiple Imputation: New Developments

Abstract
In this chapter, we discuss new developments in the field of multiple imputation. Our focus will be on non-standard missing data problems, i.e., we focus on situations, where distributional assumptions of standard MI approaches (for example, multivariate normality or homoscedasticity) are violated, and outline solutions to these problems.
Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Backmatter

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