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Exploring Methods to Investigate Sentencing Decisions

Author

Listed:
  • Elizabeth L. C. Merrall

    (MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, United Kingdom, elizabeth.merrall@mrc-bsu.cam.ac.uk)

  • Mandeep K. Dhami

    (University of Cambridge, Institute of Criminology, Faculty of Law, Sidgwick Avenue, Cambridge, United Kingdom)

  • Sheila M. Bird

    (MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, United Kingdom, Department of Statistics and Modelling Science, Strathclyde University, Glasgow, United Kingdom)

Abstract

The determinants of sentencing are of much interest in criminal justice and legal research. Understanding the determinants of sentencing decisions is important for ensuring transparent, consistent, and justifiable sentencing practice that adheres to the goals of sentencing, such as the punishment, rehabilitation, deterrence, and incapacitation of the offender, as well as reparation for the victim. It is important to frame research questions on sentencing that can feasibly be answered by appropriate research methods, within the constraints of limited time and resources. For illustration, this article presents three methodological approaches for investigating the factors that may influence sentencing decisions: multilevel analysis using existing sentencing data; sampling of, and data collection from, sentenced court case files; and experimental designs involving sentencers deciding on hypothetical cases. The strengths and weaknesses of each approach are compared and discussed.

Suggested Citation

  • Elizabeth L. C. Merrall & Mandeep K. Dhami & Sheila M. Bird, 2010. "Exploring Methods to Investigate Sentencing Decisions," Evaluation Review, , vol. 34(3), pages 185-219, June.
    • Handle: RePEc:sae:evarev:v:34:y:2010:i:3:p:185-219
    DOI: 10.1177/0193841X10369624
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    References listed on IDEAS

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    1. Dorta-Guerra, Roberto & González-Dávila, Enrique & Ginebra, Josep, 2008. "Two-level experiments for binary response data," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 196-208, September.
    2. Dror, Hovav A. & Steinberg, David M., 2008. "Sequential Experimental Designs for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 288-298, March.
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    Cited by:

    1. Czarnocki, Kazimierz & Janulek, Dawid & Olejnik, Łukasz, 2019. "When stealing, go for millions? Quantitative analysis of white-collar crime sentencing in Poland," MPRA Paper 92340, University Library of Munich, Germany.

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