May 1, 2024
Updated June 6, 2025
18 minute read
Missing Data: Understanding and Addressing the Gaps in Your Information
r1zora|
Find a path to becoming a Missing Data. Learn more at:
OpenCourser.com/topic/r1zora/missing
Reading list
We've selected 25 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Missing Data.
Provides a comprehensive overview of statistical methods for missing data, including multiple imputation, maximum likelihood estimation, and Bayesian methods. It valuable resource for anyone working with missing data.
Provides a comprehensive overview of missing data in longitudinal studies, including methods for handling missing data and assessing the impact of missing data on study results. It is written by two leading experts in the field and is essential reading for anyone working with longitudinal data.
Provides a comprehensive overview of multiple imputation for nonresponse in surveys, including methods for implementing multiple imputation and assessing the impact of missing data on survey results. It is written by two leading experts in the field and valuable resource for anyone working with missing data in surveys.
Considered a foundational text in the field, this book provides a comprehensive theoretical framework for understanding and handling missing data. It covers various methods, including maximum likelihood and multiple imputation, and is suitable for those seeking a deep understanding of the statistical principles involved. is often used as a textbook in graduate-level statistics programs and valuable reference for researchers.
Offers a practical and accessible approach to missing data analysis, focusing on applying methods in real-world research. It covers a range of techniques, including multiple imputation and maximum likelihood, with clear explanations and examples. This book is highly recommended for applied researchers and graduate students who need to implement missing data procedures in their work. The second edition includes recent developments and expanded coverage of Bayesian estimation and other timely topics.
Focusing specifically on multiple imputation, this book provides a detailed guide to this powerful technique for handling missing data. The author key developer of the MICE package in R, and the book includes practical guidance and examples using this software. is particularly useful for researchers and practitioners who plan to use multiple imputation in their data analysis. The second edition incorporates recent developments in the field.
Covers up-to-date statistical theories and computational methods for analyzing incomplete data. It delves into topics such as likelihood-based approaches, imputation techniques (including fractional imputation), propensity score methods, and methods for nonignorable missing data. It is aimed at researchers and graduate students in statistics and includes real and simulated data examples.
Specifically addresses missing data issues within the context of clinical trials. It covers the challenges and appropriate methods for handling missing data in this specific domain, which aligns with the healthcare data analysis aspect of the provided course names. It's a valuable resource for researchers and statisticians working in clinical research.
Authored by one of the pioneers of multiple imputation, this classic text lays the groundwork for the theory and application of MI, particularly in the context of survey data. While the context is surveys, the principles are broadly applicable. is highly recommended for those interested in the origins and theoretical justification of multiple imputation.
Offers practical methods for analyzing missing data, emphasizing the reasoning behind the techniques. It covers both frequentist and Bayesian perspectives and provides guidance on pragmatic issues such as selecting the number of imputations. This book valuable resource for applied statisticians and researchers looking for practical advice and a bridge between design-based and model-based perspectives.
This concise book provides a clear and intuitive introduction to the problem of missing data and common techniques for addressing it. It offers a non-technical explanation of methods like listwise deletion, maximum likelihood, and multiple imputation. is an excellent starting point for beginners and researchers in social sciences who need a foundational understanding without getting bogged down in complex statistical theory.
Provides a detailed account of multiple imputation and its applications. While one review noted potential issues with clarity in an earlier edition, the topic and authors are highly relevant to contemporary missing data practice. It is suitable for researchers and practitioners focusing on applying multiple imputation.
Aimed at non-statisticians in social, behavioral, and health sciences, this book provides practical information on implementing modern missing data procedures. It offers an accessible introduction to the theory, step-by-step instructions for multiple imputation, and advice for avoiding problems. It also includes discussions on attrition and planned missing data designs, making it useful for both beginners and more advanced readers.
Presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering continuous, categorical, and mixed datasets. It focuses on making missing-data tools accessible and bridges the gap between theory and practice. While published in 1997, it remains a valuable resource for understanding likelihood-based and Bayesian approaches.
Delves into the theoretical aspects of handling missing data using semiparametric models. It more advanced text, suitable for those with a strong statistical background interested in the theoretical underpinnings of missing data methods. It covers topics like generalized estimating equations and Cox's proportional hazards model in the context of missing data.
This online resource provides a practical introduction to exploring, considering, and dealing with missing values, with a focus on using R. It covers finding, exploring, cleaning, investigating the reasons for missingness, and imputing missing values. It is suitable for those with basic R experience and offers practical guides and exercises.
While not solely focused on missing data, this comprehensive book on Bayesian data analysis includes significant discussion on handling missing data within a Bayesian framework. Given the mention of Bayesian statistics in the course names, this book would be a valuable resource for understanding how Bayesian methods can be applied to missing data problems. It widely recognized text in Bayesian statistics.
Focuses on the analysis of longitudinal data, which often involves missing values. While not exclusively about missing data, it provides essential background and methods for analyzing data collected over time, including techniques for handling incomplete longitudinal data. It is highly relevant for those working with repeated measurements in healthcare or social sciences.
Survival analysis, which deals with time-to-event data, often encounters missing data in the form of censoring. provides a comprehensive guide to survival analysis methods, including how to handle censoring, which specific type of missing data. It is relevant for those working with time-to-event data in clinical or health research.
Generalized Estimating Equations (GEE) are a common approach for analyzing correlated data, such as longitudinal or clustered data, and methods exist for handling missing data within the GEE framework. provides a detailed explanation of GEE, which would be beneficial for understanding one approach to analyzing data with missingness in longitudinal studies.
Offers a practical introduction to Bayesian statistics using R and Stan. While not exclusively about missing data, it covers Bayesian modeling in a way that can be applied to problems with missing values. Given the mention of Bayesian statistics in the course names, this book provides a hands-on approach to Bayesian methods relevant to missing data imputation and analysis.
Missing data can complicate causal inference. provides a foundational understanding of causal inference, which is essential when dealing with missing data in studies aiming to determine cause-and-effect relationships. While not solely focused on missing data, the principles of causal inference are crucial for appropriately handling missingness in causal analysis.
This practical machine learning book covers various techniques and includes discussions on handling missing values within the context of building machine learning models. While not a deep dive into missing data theory, it provides practical approaches for dealing with missingness in a machine learning workflow, which is relevant for data science practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/r1zora/missing
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