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An annoying paradox which has been facing policy-makers and practitioners alike in their efforts to deal with public problem drinkers is that of recognizing the condition of alcohol-related offenders as both a misdemeanor and a disease. This paradox is discussed in terms of a legal model and a therapeutic model for responding to alcohol-related problems. A multivariate covariance model is used to compare the effectiveness of treatment for problem drinkers coerced into treatment by the courts and problem drinkers voluntarily initiating treatment, while statistically controlling for pre-treatment group differences. With regards to effectiveness, and the role played by an element of coercion in effectiveness, type of referral did have an important independent impact upon treatment outcome. An element of coercion involved in that referral did not subvert the goals of the therapeutic model; indeed, it rendered successful treatment outcome considerably more likely than with strictly voluntary self-referral. Finally, coercion proved somewhat more effective where the penalties for non-compliance were the more certain, not necessarily the more severe.

A common and perverse problem facing those who evaluate human service programs is the difficulty in getting random assignment of clients to control and experimental groups, so that experimental or quasi-experimental research designs can be meaningfully applied. This article demonstrates the use of a technique (covariance adjustment) that statistically manipulates independent variables so as to give an approximation of random assignment on those variables. In this article, we adapt the work of Alwin and Sullivan (1976) to an actual data set from public alcohol treatment programs. We found some very significant differences between the adjusted and nonadjusted treatment out comes, demonstrating the need for some type of pretreatment controls in the absence of random assignment. The covariance adjustment technique and its assumptions are dis cussed leading to the conclusion that the technique is a very workable resolution of the random assignment problem. We also demonstrate how the technique yields some valuable information generally not available when random assignment is used: namely, the identification and weighting of certain selection biases as they relate to the dependent variable.