Assessing the accuracy of individual link with varying block sizes and cut-off values using MaCSim approach
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Record linkage is the process of matching together the records from different data sources that belong to the same entity. Record linkage is increasingly being used by statistical, health, government and business organisations to link administrative, survey, population census and other files to create a robust file for more complete and comprehensive analysis. Despite this increase, there has been little work on developing tools to assess the accuracy of linked files. Ensuring that the matched records in the combined file correspond to the same individual or entity is crucial for the validity of any analyses and inferences based on the combined data. Haque et al. (submitted) proposed a Markov Chain based Monte Carlo simulation approach (MaCSim) for assessing a linking method and applied synthetic data provided by the Australian Bureau of Statistics (ABS) to illustrate the utility of the approach based on realistic data settings. The defined linking method employed by MaCSim is assessed by the proportion of correct links for each record. Different blocking strategies are considered to classify matches from non-matches with different levels of accuracy. In this paper, MaCSim method is utilized to evaluate the accuracy of individual link using varying block sizes and different cut-off values while minimizing error. The aim is to facilitate optimal choice of block size and cut-off value to achieve high accuracy in terms of minimizing the average False Discovery Rate (FDR) and False Negative Rate (FNR). The analyses indicated promising results for finding the optimal cut-off value as well as efficient block size.
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