Improved Assessment of the Accuracy of Record Linkage via an Extended MaCSim Approach

Abstract

Abstract Record linkage is the process of bringing together the same entity from overlapping data sources while removing duplicates. Huge amounts of data are now being collected by public or private organizations as well as by researchers and individuals. Linking and analysing relevant information from this massive data reservoir can provide new insights into society. It has become increasingly important to have effective and efficient methods for linking data from different sources. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or to compare between methods with respect to accuracy. In this article, we improve on a Markov Chain based Monte Carlo simulation approach (MaCSim) for assessing a linking method. The improvement proposed here involves calculation of a similarity weight for every linking variable value for each record pair, which allows partial agreement of the linking variable values. To assess the accuracy of the linking method, correctly linked proportions are investigated for each record. The extended MaCSim approach is illustrated using a synthetic data set provided by the Australian Bureau of Statistics based on realistic data settings. Test results show high accuracy of the assessment of the linkages.