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This book helps practitioners gain a deeper understanding, at an applied level, of the issues involved in improving data quality through editing, imputation, and record linkage. The first part of the book deals with method and models. Here, we focus on the Fellegi-Holt edit-imputation model, the Little-Rubin multiple-imputation scheme, and the Fellegi-Sunter record linkage model. Brief examples are included to show how these techniques work. In the second part of the book, the authors present real-world case studies in which one or more of these techniques are used. They cover a wide variety of application areas. These include mortgage guarantee insurance, medical, biomedical, highway safety, and social insurance as well as the construction of list frames and administrative lists. There are no other books available addressing this subject.

Índice: From the contents Introduction.- What is Data Quality and why Should We Care?.- Examples of Companies Using Data to their Advantage/Disadvantage.- Properties of Data Quality and Metrics for Measuring it.- Basic Data Quality Tools.- Mathematical Preliminaries for Specialized Data Quality Techniques.- Automatic Editing and Imputation of Survey Data-A Unified Approach to Identifying and Correcting Data Problems in Sample Surveys.- Record Linkage--Methodology.- Estimating the Parameters of Fellegi-Sunter Record Linkage Model.- Standardization and Parsing.- Phonetic Coding Systems for Names and/Or Addresses.- B... Etc.