Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies

Multiple Imputation of Missing Data in Practice provides a comprehensive introduction to the multiple imputation approach to missing data problems, with examples from real data and simulation studies. It is accessible to a broad audience and covers statistical concepts, models, and methods, including univariate and multivariate missing data, survival analysis, longitudinal data, complex surveys, and measurement error.

Format: Hardback
Length: 476 pages
Publication date: 26 November 2021
Publisher: Taylor & Francis Inc

Offers practical guidance on how to address missing data problems using multiple imputation,including step-by-step instructions and examples.

Explains the basic theory behind multiple imputation and many commonly-used models and methods,illustrated by examples from a wide variety of missing data problems.

Uses real data from studies with different designs and features to demonstrate the methods,with example datasets and sample programming code either included in the book or available at a github site.

Helps readers not only to know how to use the methods,but also to understand why multiple imputation works and how to choose appropriate methods.

Is a valuable resource for researchers,statisticians,data analysts,and practitioners who encounter missing data problems in their work.
Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies is a comprehensive guide to the multiple imputation approach, a widely used method for handling missing data in data analysis. Over the past four decades, multiple imputation has undergone rapid development, becoming the most versatile, popular, and effective strategy for addressing missing data issues across various fields. This book aims to provide a solid foundation for understanding and applying multiple imputation in research and practical settings.

Accessible to a broad audience, the book begins by introducing statistical concepts related to missing data problems and their associated terminology. It then focuses on how to address missing data issues using multiple imputation, providing step-by-step instructions and examples throughout. The basic theory behind multiple imputation is explained, along with various commonly-used models and methods. These concepts are illustrated with real-world examples from diverse fields, such as cross-sectional data, longitudinal data, complex surveys, survival data, and studies subject to measurement error.

To enhance readers' understanding and practical application of multiple imputation, simulation studies are employed to assess the performance of different methods. Example datasets and sample programming code are either included in the book or readily available at a GitHub site.

Key features of this book include:

An overview of statistical concepts that are essential for better understanding missing data problems and multiple imputation analysis.

Practical guidance on how to address missing data problems using multiple imputation, including step-by-step instructions and examples.

Exploration of the basic theory behind multiple imputation, along with numerous commonly-used models and methods, illustrated with examples from a wide range of missing data problems.

Use of real data from studies with different designs and features to demonstrate the methods, with example datasets and sample programming code either included in the book or accessible at a GitHub site.

Assistance in helping readers not only to learn how to use multiple imputation methods but also to gain insights into their workings and how to select appropriate methods for specific data analysis tasks.

This book is a valuable resource for researchers, statisticians, data analysts, and practitioners who encounter missing data problems in their work. Its comprehensive coverage of multiple imputation theory, methods, and applications makes it an essential tool for anyone seeking to improve the accuracy and reliability of their data analysis.

Dimension: 234 x 156 (mm)
ISBN-13: 9781498722063