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Data Quality and Record Linkage Techniques
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Table of Contents

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 Databreak 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 Fellegi-Sunter 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.

Reviews

From the reviews:

"Data Quality and Record Linkage Techniques is a landmark publication that will facilitate the work of actuaries and other statistical professionals." Douglas C. Borton for The Actuarial Digest

"This book is intended as a primer on editing, imputation and record linkage for analysts who are responsible for the quality of large databases. ... The book provides an extended bibliography with references ... . The examples given in the book can be valuable for organizations responsible for the quality of databases, in particular when these databases are constructed by linking several different data sources." (T. de Waal, Kwantitatieve Methoden, October, 2007)

"Tom Herzog has a history of writing books...that most mathematically literate people believe they already understand pretty well--until they read the book....This book...[is] interesting and informative. Anyone who works with large databases should read it." (Bruce D. Schoebel, Contingencies, Jan/Feb 2008)

"Who should read this book? The short answer is everyone who is concerned about data quality and what can be done to improve it. Buy a copy for yourself; buy another copy for your IT support." (Kevin Pledge, CompAct, October 2007)

"Data Quality and Record Linkage Techniques is one of the few books on data quality and record linkage that try to cover and discuss the possible errors in different types of data in practical situations. ... The intended audience consists of actuaries, economists, statisticians and computer scientists. ... This is a good short book for an overview of data quality problems and record linkage techniques. ... Statisticians, data analysts and indeed anyone who is going to collect data should first read this book ... ." (Waqas Ahmed Malik and Antony Unwin, Psychometrika, Vol. 73 (1), 2008)

"This book covers two related and important topics: data quality and record linkage. ... case studies are the book's major strength; they contain a treasure trove of useful guidelines and tips. For that reason, the book is an excellent purchase for practitioners in business, government, and research settings who plan to undertake major data collection or record linkage efforts. ... serves as a stand-alone resource on record linkage techniques. ... The book is aimed squarely at practitioners." (Jerome Reiter, Journal of the American Statistical Association, Vol. 103 (482), 2008)

"The book provides a good, sound, verbal introduction and summary, and a useful point of departure into the more technical side of database quality and record linkage problems. In summary, it should be a core sourcebook for non-mathematical statisticians in official statistics agencies, and database designers and managers in government and commerce. It also provides a useful introduction to this important topic, and a comprehensive reference list for further study, for professional statisticians and academics." (Stephan Haslett, International Statistical Reviews, Vol. 76 (2), 2008)

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