Data Quality and Record Linkage Techniques

This book helps practitioners gain a working 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. Brief examples are included to show how these techniques work. The second part of the book presents real-world case studies in which one or more of these techniques are applied. These cover a wide variety of application areas, including mortgage guarantee insurance, medical, biomedical, highway safety, and social insurance as well as the construction of list frames and administrative lists. The authors also discuss software that has been developed to apply the techniques described here.

Readers will find in this book a mixture of practical advice, mathematical rigor, management insight and philosophy.

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 methods 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.

Readers will find this book a mixture of practical advice, mathematical rigor, management insight and philosophy. The long list of references at the end of the book enables readers to delve more deeply into the subjects discussed here. The authors also discuss the software that has been developed to apply the techniques described in our text.

Thomas N. Herzog, Ph.D., ASA is the Chief Actuary at the U.S. Department of Housing and Urban Development. He holds a Ph.D. in mathematics from the University of Maryland and is also an Associate of the Society of Actuaries. He is the author or co-author of books on Credibility Theory, Monte Carlo Methods, and Models for Quantifying Risk.

Fritz J. Scheuren, Ph.D., is a Vice President for Statistics with the National Opinion Research Center at the University of Chicago. He has a Ph.D. in statistics from the George Washington University. He is much published with over 300 papers and monographs. He is the 100th President of the American Statistical Association and a Fellow of both the American Statistical Association and the American Association for the Advancement of Science.

William E. Winkler, Ph.D., is Principal Researcher at the U.S. Census Bureau. He holds a Ph.D. in probability theory from Ohio State University and is a Fellow of the American Statistical Association. He has more than 130 papers in areas such as automated record linkage and data quality. He is the author or co-author of eight generalized software systems, some of which are used for production in the largest survey and administrative-list situations.