Citation

Peck RC, Arstein-Kerslake GW, Helander CJ. J. Stud. Alcohol 1994; 55(6): 667-678.

Affiliation

Department of Motor Vehicles, Program and Policy Administration, Sacramento, California 95818.

Copyright

(Copyright © 1994, Rutgers Center of Alcohol Studies)

DOI

unavailable

PMID

7861794

Abstract

The primary objective of this study was to assess the extent to which drunk-driving (DUI) recidivism and DUI treatment program compliance could be predicted from psychometric, biographical, drinking history and prior-driving-record variables. These analyses were performed on data from 7,316 DUI offenders initially collected in Sacramento County, California, from September 1977 through January 1981. For most analyses, the recidivism measure was a composite of major convictions (DUI, reckless, hit-and-run), nighttime (6 PM-6 AM) and alcohol-related accidents during the 4-year interval following treatment assignment. The prediction of recidivism was highly significant for both the construct sample and the 25% cross-validation sample. The predictive accuracy was low, however, as evidenced by multiple Rs of < .30. The predicted rates of recidivism generated for each individual by the regression equation were cross tabulated by other criteria of interest, including total accidents and total injury and fatal accidents. Offenders at high risk of recidivating had substantially higher rates of accidents. The results indicate that reasonably accurate prediction of recidivism is only possible for discriminating between offenders at the extremes of the recidivism expectancy distribution. The above approach was also used to isolate factors predictive of program compliance (successfully completing treatment). In all cases, the prediction of compliance was highly statistically significant. In general, compliance was much more predictable than was subsequent DUI recidivism. Those offenders having a high probability of being noncompliant were much more likely to recidivate and have accidents than were those with favorable compliance expectancies.