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

Document Type : Original Scientific Paper

Author

Iranian Judiciary Research Institute, Tehran, Iran.

Abstract

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

Keywords

References

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Journal of Statistical Modelling: Theory and Applications
Volume 2, Issue 2
July 2021
Pages 143-158

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History

  • Receive Date: 25 April 2022
  • Revise Date: 19 June 2022
  • Accept Date: 26 June 2022

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