Regression Modeling and File Matching Using Possibly Erroneous Matching Variables. Issue 4 (2nd October 2018)

• Record Type: Journal Article
• Authors: Dalzell, Nicole M.; Reiter, Jerome P.
• Is Part Of: Journal of computational and graphical statistics. Volume 27:Issue 4(2018)
• Page Start: 728
• Page End: 738
• Publication Date: 2018-10-02
• Subjects: Fusion -- Imputation -- Mixture -- Record linkage; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2018.1458624
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Many analyses require linking records from two databases comprising overlapping sets of individuals. In the absence of unique identifiers, the linkage procedure often involves matching on a set of categorical variables, such as demographics, common to both files. Typically, however, the resulting matches are inexact: some cross-classifications of the matching variables do not generate unique links across files. Further, the variables used for matching can be subject to reporting errors, which introduce additional uncertainty in analyses. We present a Bayesian file matching methodology designed to estimate regression models and match records simultaneously when categorical variables used for matching are subject to errors. The method relies on a hierarchical model that includes (1) the regression of interest involving variables from the two files given a vector indicating the links, (2) a model for the linking vector given the true values of the variables used for matching, (3) a model for reported values of the variables used for matching given their true values, and (4) a model for the true values of the variables used for matching. We describe algorithms for sampling from the posterior distribution of the model. We illustrate the methodology using artificial data and data from education records in the state of North Carolina.