Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity

Pina-Sánchez, J and Gosling, JP orcid.org/0000-0002-4072-3022 (2020) Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity. Quality & Quantity. ISSN 0033-5177

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Copyright, Publisher and Additional Information: © 2020, Springer Science and Business Media LLC. This is an author produced version of a paper published in Quality & Quantity. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Sentencing; Selection bias; Severity; Paired comparison; Bayesian statistics; Tobit models
Dates:
  • Accepted: 4 February 2020
  • Published (online): 14 February 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds)
Funding Information:
FunderGrant number
ESRCNot Known
Depositing User: Symplectic Publications
Date Deposited: 17 Feb 2020 12:44
Last Modified: 30 Jun 2020 14:50
Status: Published online
Publisher: Springer Science and Business Media LLC
Identification Number: https://doi.org/10.1007/s11135-020-00973-z

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