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Short description:

This book provides a comprehensive overview of the entire edit and imputation process for detecting and correcting errors in survey research.

Long description:

This book provides a comprehensive overview of the entire edit and imputation process for detecting and correcting errors in survey research. The authors begin with an introduction to the problem of errors and missing values in survey data and then go on to explore the methods for correcting systematic errors, identifying random errors, and error localization in numerical and categorical data. Next, an intricate discussion of selective editing outlines various mechanisms for identifying the appropriate resources for treating data errors. A basic framework for imputation is provided in the next chapter with a breakdown of key methods and models along with a comparison of imputation with the weighting approach to correct missing values. The remaining chapters delve into more advanced topics in imputation methodology as well as new developments on imputation under edit constraints and benchmarking. Each chapter organizes the presented information in uniform components, with an introduction, outline of key theory and formulae, illustration of algorithms, a concise summary of key points, and a reference section listing additional resources on the topic. This presentation solidifies the book’s goal of serving as a practical, one-stop reference on data editing and imputation.

A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques

Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. Handbook of Statistical Data Editing and Imputation equips readers with the essential statistical procedures for detecting and correcting inconsistencies and filling in missing values with estimates. The authors supply an easily accessible treatment of the existing methodology in this field, featuring an overview of common errors encountered in practice and techniques for resolving these issues.

The book begins with an overview of methods and strategies for statistical data editing and imputation. Subsequent chapters provide detailed treatment of the central theoretical methods and modern applications, with topics of coverage including:

• Localization of errors in continuous data, with an outline of selective editing strategies, automatic editing for systematic and random errors, and other relevant state-of-the-art methods

• Extensions of automatic editing to categorical data and integer data

• The basic framework for imputation, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values

• More advanced imputation methods, including imputation under edit restraints

Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. The discussion concludes with a summary of the main concepts and a real-world example that incorporates realistic data along with professional insight into common challenges and best practices.

Handbook of Statistical Data Editing and Imputation is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.