  Record linkage is the process of matching together records from different
data sources that belong to the same entity. Record linkage is increasingly
being used by many organizations including statistical, health, government etc.
to link administrative, survey, and other files to create a robust file for
more comprehensive analysis. Therefore, it becomes necessary to assess the
ability of a linking method to achieve high accuracy or compare between methods
with respect to accuracy. In this paper, we evaluate the accuracy of individual
link using varying block sizes and different cut-off values by utilizing a
Markov Chain based Monte Carlo simulation approach (MaCSim). MaCSim utilizes
two linked files to create an agreement matrix. The agreement matrix is
simulated to generate re-sampled versions of the agreement matrix. A defined
linking method is used in each simulation to link the files and the accuracy of
the linking method is assessed. The aim of this paper is to facilitate optimal
choice of block size and cut-off value to achieve high accuracy in terms of
minimizing average False Discovery Rate and False Negative Rate. The analyses
have been performed using a synthetic dataset provided by the Australian Bureau
of Statistics (ABS) and indicated promising results.
