This book offers a practical understanding of issues involved in improving data quality through editing, imputation, and record linkage. The first part of the book deals with methods and models, focusing on the Fellegi-Holt edit-imputation model, the Little-Rubin multiple-imputation scheme, and the Fellegi-Sunter record linkage model. The second part presents case studies in which these techniques are applied in a variety of areas, including mortgage guarantee insurance, medical, biomedical, highway safety, and social insurance as well as the construction of list frames and administrative lists. This book offers a mixture of practical advice, mathematical rigor, management insight and philosophy.

Data Quality: What It is, Why It is Important, and How to Achieve It.- What is Data Quality and Why Should We Care?.- Examples of Entities Using Data\break to their Advantage/Disadvantage.- Properties of Data Quality and Metrics for Measuring It.- Basic Data Quality Tools.- Specialized Tools for Database Improvement.- Mathematical Preliminaries for Specialized Data Quality Techniques.- Automatic Editing and Imputation of Sample Survey Data.- Record Linkage Methodology.- Estimating the Parameters of the FellegiSunter Record Linkage Model.- Standardization and Parsing.- Phonetic Coding Systems for Names.- Blocking.- String Comparator Metrics for Typographical Error.- Record Linkage Case Studies.- Duplicate FHA Single-Family Mortgage Records.- Record Linkage Case Studies in the Medical, Biomedical, and Highway Safety Areas.- Constructing List Frames and Administrative Lists.- Social Security and Related Topics.- Other Topics.- Confidentiality: Maximizing Access to Micro-data while Protecting Privacy.- Review of Record Linkage Software.- Summary Chapter.