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Record linkage is the process of bringing together the same entity fromoverlapping data sources while removing duplicates. Huge amounts of data arenow being collected by public or private organizations as well as byresearchers and individuals. Linking and analysing relevant information fromthis massive data reservoir can provide new insights into society. However,this increase in the amount of data may also increase the likelihood ofincorrectly linked records among databases. It has become increasinglyimportant to have effective and efficient methods for linking data fromdifferent sources. Therefore, it becomes necessary to assess the ability of alinking method to achieve high accuracy or to compare between methods withrespect to accuracy. In this paper, we improve on a Markov Chain based MonteCarlo simulation approach (MaCSim) for assessing a linking method. MaCSimutilizes two linked files that have been previously linked on similar types ofdata to create an agreement matrix and then simulates the matrix using aproposed algorithm developed to generate re-sampled versions of the agreementmatrix. A defined linking method is used in each simulation to link the filesand the accuracy of the linking method is assessed. The improvement proposedhere involves calculation of a similarity weight for every linking variablevalue for each record pair, which allows partial agreement of the linkingvariable values. A threshold is calculated for every linking variable based onadjustable parameter "tolerance" for that variable. To assess the accuracy oflinking method, correctly linked proportions are investigated for each record.The extended MaCSim approach is illustrated using a synthetic dataset providedby the Australian Bureau of Statistics (ABS) based on realistic data settings.Test results show higher accuracy of the assessment of linkages.

Improved assessment of the accuracy of record linkage via an extended MaCSim approach