Journal of Quantitative Criminology

, Volume 8, Issue 2, pp 217–232 | Cite as

A model for ranking the punitiveness of the states

  • William L. Selke
  • Steen A. Andersson
Article

Abstract

There has recently been much interest in the measurement of imprisonment rates. Since this variable has such widespread importance in criminological research and policy, new methods are called for in expanding the procedures for evaluating levels of punitiveness as indicated by imprisonment rates. This paper presents a new model using logarithmic transformations to develop a system for ranking the punitiveness of the states. Comparisons are made between different approaches to specifying imprisonment rates including controls for crime rates and arrest rates. Results of the analyses indicate that the use of this model generates somewhat different rankings of punitiveness compared with those based on sample imprisonment rates or prisoner/arrest ratios.

Key words

punitiveness imprisonment rates logarithmic transformation state rankings 

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References

  1. Anderson, T. W. (1984).An Introduction to Multivariate Statistical Analysis, John Wiley & Sons, New York.Google Scholar
  2. Austin, J., and Tillman, R. (1988).Ranking the Nation's Most Punitive States, National Council on Crime and Delinquency, San Francisco.Google Scholar
  3. Bureau of Justice Statistics (1988).Prisoners in 1987, U.S. Department of Justice, Washington, DC.Google Scholar
  4. Duffee, D. E. (1989).Corrections: Policy and Practice, Random House, New York.Google Scholar
  5. Federal Bureau of Investigation (1988).Crime in the United States, 1987, U.S. Government Printing Press, Washington, DC.Google Scholar
  6. Gaes, G. G., and McGuire, W. (1985). Prison violence: The contribution of crowding versus other determinants of prison assault rates.J. Res. Crime Deling. 22: 41–65.Google Scholar
  7. Garofalo, J. (1987).Measuring the Use of Imprisonment, National Institute of Justice, Washington, DC.Google Scholar
  8. Gottfredson, S. D., and Taylor, R. B. (1984). Public policy and prison populations: Measuring opinions about reform.Judicature 68: 190–201.Google Scholar
  9. Lehmann, E. L. (1983).Theory of Point Estimation, John Wiley & Sons, New York.Google Scholar
  10. Lehmann, E. L. (1986).Testing Statistical Hypotheses, John Wiley & Sons, New York.Google Scholar
  11. McGarrell, E. F., and Castellano, T. C. (1991). An integrative conflict model of the criminal law formation process.J. Res. Crime Delinq. 28: 174–196.Google Scholar
  12. Michalowski, R. J., and Pearson, M. A. (1990). Punishment and social structure at the state level: A cross-sectional comparison of 1970 and 1980.J. Res. Crime Delinq. 27: 52–78.Google Scholar
  13. Riley, P. J., and Rose, V. M. (1980). Public and elite opinion concerning correctional reform.J. Crim. Just. 8: 345–356.Google Scholar
  14. Siegel, S. (1956).Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill, New York.Google Scholar
  15. Zimmerman, S. E., Van Alystyne, D. J., and Dunn, C. S. (1988). The National Punishment Survey and public policy consequences.J. Res. Crime Delinq. 25: 120–149.Google Scholar
  16. Zimring, F. E., and Hawkins, G. (1991).The Scale of Imprisonment, University of Chicago Press, Chicago.Google Scholar

Copyright information

© Plenum Publishing Corporation 1992

Authors and Affiliations

  • William L. Selke
    • 1
  • Steen A. Andersson
    • 2
  1. 1.Department of Criminal JusticeIndiana UniversityBloomington
  2. 2.Department of MathematicsIndiana UniversityBloomington

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