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Year 2016 x

    Quantitative empirical research into legal decisions must be conducted using statistical tools that are appropriate for the data involved. Court decisions are one example of a domain where the data is intrinsically hierarchical (i.e., multilevel), since decisions are made on individual cases by decision-makers in courts located across geographical (or jurisdictional) areas. Past research into court decisions has often either neglected higher level variables or incorrectly used single-level statistical models to analyze multilevel data. The lack of a clear understanding about when and why multilevel statistical models are required may have contributed to this situation. In this paper, we identify the problems of estimating single-level models on hierarchically structured data, and consider the advantages of conducting multilevel analyses under these circumstances. We use the example of criminal sentencing research to illustrate the arguments for the use of multilevel models and against a single-level approach. We also highlight some issues to be addressed in future sentencing studies.


Mandeep Dhami

Ian Keith Belton
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