Data Quality and Record Linkage Techniques

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Data Quality and Record Linkage Techniques. The accuracy of t he Cont ent should not be relied upon and should be independent ly verified wit h prim ary sources of inform at ion. Taylor and Francis shall not be liable for any losses, act ions, claim s, proceedings, dem ands, cost s, expenses, dam ages, and ot her liabilit ies what soever or howsoever caused arising direct ly or indirect ly in connect ion wit h, in relat ion t o or arising out of t he use of t he Cont ent.

This art icle m ay be used for research, t eaching, and privat e st udy purposes. Any subst ant ial or syst em at ic reproduct ion, redist ribut ion, reselling, loan, sub- licensing, syst em at ic supply, or dist ribut ion in any form t o anyone is expressly forbidden. The second part describes record linkage. These chapters are su- participation rates in the groups can give rise to what appears to be discrim- perbly written.

The authors explain the Fellegi—Sunter approach to determining ination, even if there is none. I especially appreciate the chapters on the nuts-and-bolts as- the multiple pools comparison. Reading the advice in these chapters is sure to help prac- in his courtroom experience is that they are often inappropriate for the cases titioners improve their record linkage projects. The third part presents some being considered. His typical frustration is with experts who fail to test their extended case studies in data quality practice and record linkage.

The or to improperly applied regression models.

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I found this element of the book fourth part contains two chapters: an overview of techniques for providing data repetitive, and he is preaching to the choir. Admittedly, more is at stake in large lawsuits than in academic papers, of record linkage software. The book has ample coverage of topics in record linkage, and as such serves In Dr. Its coverage of data finer points of statistical arguments; what a judge should be expected to evalu- quality is less exhaustive.

The authors do not delve into classic survey error ate is whether the analysis answers the relevant questions. In Dr.

Thus experience, many experts present analyses that do not answer the questions at I recommend that readers planning major data collection efforts supplement Downloaded by [University of New Hampshire] at 04 October hand. A judge should be able to assess when this is happening, drawing from this book with one of the main texts on data quality, such as those by Groves his or her legal education in analyzing the logic of an argument. The greatest contribution of The Expert is in the final chapter, where it The book is aimed squarely at practitioners.

Data Quality and Record Linkage Techniques

Most of it requires nothing more presents an alternative to the current standards for whether scientific expertise than an elementary mathematical background. The one exception is the discus- should be accepted in the courtroom. This frames the ques- putations for large databases. Another type of expert, the ondary users.

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Perhaps most im- the same way in which scientific expertise is judged, the argument is instead portantly, we in the statistical and survey communities have yet to develop a rig- weighed based on an evaluation of how often the craft expert has been right orous, mathematical foundation for data quality. How do we quantify the quality historically. I suspect that many survey methodologists would agree that judgment in similar cases has been shown to be correct.

Taken to an extreme, dealing with all sources of errors is the greatest unsolved problem in survey the line can blur i. Applying Statistics in the Courtroom is Duke University geared as an introduction, more interested in providing vocabulary than pro- moting an understanding of statistics or expressing strong opinions.

Adjusting the Weights for the Winkler Comparator Metric Where are We Now? Duplicate Mortgage Records Mortgage Records with an Incorrect Termination Status Estimating the Number of Duplicate Mortgage Records Biomedical and Genetic Research Studies Who goes to a Chiropractor? National Master Patient Index Crash Outcome Data Evaluation System Constructing List Frames and Administrative Lists. National Address Register of Residences in Canada Social Security and Related Topics. Record Linkage and Terrorism Documenting Public-use Files Checking Re-identifiability Elementary Masking Methods and Statistical Agencies Protecting Confidentiality of Medical Data More-advanced Masking Methods — Synthetic Datasets Review of Record Linkage Software.

Checklist for Evaluating Record Linkage Software Summary Chapter. Herzog, Ph.

Data Quality and Record Linkage Techniques

He holds a Ph. He has devoted a major effort to improving the quality of the databases of the Federal Housing Administration. Fritz J. Scheuren, Ph. He has a Ph. He is much published with over papers and monographs.

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He has a wide range of experience in all aspects of survey sampling, including data editing and handling missing data. Much of his professional life has been spent employing large operational databases, whose incoming quality was only marginally under the control of the data analysts under his direction. His extensive work in recent years on human rights data collection and analysis, often under very adverse circumstances, has given him a clear sense of how to balance speed and analytic power within a framework of what is feasible.

William E. Winkler, Ph. He has more than 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. Caring about data quality is key to safeguarding and improving it. As stated, this sounds like a very obvious proposition. This observation becomes all the more important in this information age, when explicit and meticulous attention to data is of growing importance if information is not to become misinformation.

This chapter provides foundational material for the specifics that follow in later chapters about ways to safeguard and improve data quality. Experts on quality such as Redman [], English [], and Loshin [] have been able to show companies how to improve their processes by first understanding the basic procedures the companies use and then showing new ways to collect and analyze quantitative data about those procedures in order to improve them. Here, we take as our primary starting point primarily the work of Deming, Juran, and Ishakawa. These two criteria link the 1 It is well recognized that quality must have undoubted top priority in every organization.

As Juran and Godfrey [; pages 4—20, 4—21, and 34—9] makes clear, quality has several dimensions, including meeting customer needs, protecting human safety, and protecting the environment.

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We restrict our attention to the quality of data, which can affect efforts to achieve quality in all three of these overall quality dimensions. When used together, these two can yield efficient systems that achieve the desired accuracy level or other specified quality attributes. Unfortunately, the data of many organizations do not meet either of these criteria.

As the cost of computers and computer storage has plunged over the last 50 or 60 years, the number of databases has skyrocketed. With the wide availability of sophisticated statistical software and many well-trained data analysts, there is a keen desire to analyze such databases in-depth. Unfortunately, after they begin their efforts, many data analysts realize that their data are too messy to analyze without major data cleansing.

Currently, the only widely recognized properties of quality are quite general and cannot typically be used without further elaboration to describe specific properties of databases that might affect analyses and modeling. The seven most commonly cited properties are 1 relevance, 2 accuracy, 3 timeliness, 4 accessibility and clarity of results, 5 comparability, 6 coherence, and 7 completeness. If the data cannot presently be used for such purposes, how much time and expense would be needed to add the additional features?

A secondary or possibly primary use of a database may be better for determining what subsets of customers are more likely to purchase certain products and what types of advertisements or e-mails may be more successful with different groups of customers.

Future Directions in Probablistic Linkage

Accuracy We cannot afford to protect against all errors in every field of our database. What are likely to be the main variables of interest in our database? How accurate do our data need to be? Other sources Redman [], Wang [], Pipino, Lee, and Wang [] provide alternative lists of properties that are somewhat similar to these.

Which customers bought products 1 this week, 2 12 months ago, and 3 24 months ago? Should certain products be eliminated or added based on sales trends? Which products are the most profitable? We might be interested in demographic variables on individual voters — for example, age, education level, and income level. How accurate must the level of education variable be? Here the context might be a clinical trial in which we are testing the efficacy of a new drug.

How accurate does the measurement of the dosage level need to be? What other factors need to be measured such as other drug use or general health level because they might mitigate the efficacy of the new drug? Are all data fields being measured with sufficient accuracy to build a model to reliably predict the efficacy of various dosage levels of the new drug?

Are more stringent quality criteria needed for financial data than are needed for administrative or survey data? Timeliness How current does the information need to be to predict which subsets of customers are more likely to purchase certain products? How current do public opinion polls need to be to accurately predict election results? Are data fields e. How accurate are these identifying fields?

Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques Data Quality and Record Linkage Techniques

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