Application of hierarchical clustering on principal components to evaluate the performance of justice system by judicial indicators

The performance of justice systems is measured by empirical indicators in both developing and developed countries. The findings of existing indicator initiatives have historically been based on surveys of experts, document reviews, administrative data, or public surveys. In this paper, Principal Component Analysis (PCA) and Cluster Analysis (CA) methods were combined to resolve the problem of evaluating multiple indicators. Using PCA, this method standardizes, reduces dimensions, and decorrelates multiple indicators of evaluation of justice systems and abstracts the principal components. Then, CA is used to assign individuals (observations) to homogeneous clusters (classes). Typically, hierarchical clustering on principal components (HCPC) is employed to classify civil branches of a trial court in Iran to create a comprehensive evaluation. By applying the multivariate statistical method to data, three principal components are identified and interpreted. A hierarchical clustering algorithm is then applied, which divides the data into three clusters based on dissimilarity. These groups of the civil branches were identified based on nine judicial performance indicators. It allows policymakers and reformers to measure the performance of each branch individually, and track their progress in reducing backlogs and delays separately. As shown by the practical example, these methods are effective across justice units.