Handbook of Statistical Data Editing and Imputation

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.