New PDF release: Prediction in criminology

By David P. Farrington, Roger Tarling

ISBN-10: 088706003X

ISBN-13: 9780887060038

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The advantages of these kinds of designs have been discussed by Clarke (1976). In randomized experiments, prediction scores can be very useful in checking the success of the random assignment in producing comparable groups (see Kassebaum, Ward, and Wilner, 1971). Mannheim and Wilkins (1955) were perhaps the first to use prediction techniques in evaluating penal treatments. They compared reconviction rates of open and closed institutions within different Page 12 risk categories and found that the open institutions had consistently lower reconviction rates.

Employment for less than half of the two-year period preceding the current arrest. Each person was scored 0 or 1 according to the presence or absence of each item, leading to a prediction score between 0 and 7 for each offender. Greenwood showed how these prediction scores were related to crime rates. 9 for those scoring 4 or more. Greenwood argued that decisionmakers acting on the basis of incapacitation should be more selective: predicted high-rate offenders should receive longer prison sentences, while predicted low-rate offenders should receive shorter ones.

Evaluation of Penal Treatments and Parole Prediction scores have often been used in research evaluating the effects of different penal treatments. One of the methodological problems faced in this type of research is how to disentangle the effects of the treatments from those of the kinds of people who receive them. The best way of ensuring that comparable people receive different treatments is to assign the people at random to the treatments. However, because of ethical, legal, and practical problems, randomized experiments in criminology are often difficult to arrange (for a review, see Farrington, 1983).

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Prediction in criminology by David P. Farrington, Roger Tarling


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