Regression Modeling and File Matching Using Possibly Erroneous Matching Variables

TL;DR

This paper introduces a Bayesian approach for linking records across databases using categorical variables that may contain errors, enabling simultaneous record matching and regression analysis.

Contribution

It develops a hierarchical Bayesian model that accounts for errors in matching variables, improving record linkage and regression estimation accuracy.

Findings

Effective in handling erroneous matching variables

Improves accuracy of record linkage and regression estimates

Demonstrated on artificial and real education data

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.