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William E. Winkler and F. Scheuren, George Washington University

KEY WORDS: Edit, Imputation, Record Linkage, Regression Analysis, Recursive Processes


This paper demonstrates a methodology for analyzing two or more files when the only common information is name and address that is subject to significant error. Such a situation might arise with lists of businesses. We assume that a small proportion of records can be accurately matched. With the matched pairs we build an edit/imputation model and add predicted quantitative values, via a regression analysis to each file. Matching is then repeated with the common quantitative data and with name and address information. If necessary, the edit/impute, regression, and matching steps can be repeated in a recursive fashion. In large measure the ideas of Neter, Maynes, and Ramanathan (1965) are revised but with new tools.

Source: U.S. Census Bureau | Statistical Research Division | (301) 763-3215 (or |   Last Revised: October 08, 2010