Statistical quality control and social processes: a drug testing application
Traditional acceptance sampling procedures have been used to monitor the quality of outgoing items from a production process within a manufacturing environment. Generally, however, this has represented a reactive, rather than a proactive, approach to quality control. Importantly, the manufacturing sector has refocused itself on more proactive techniques that attempt to improve item quality by repairing the process at the closest feasible point of intervention. This type of intervention is less possible, however, in most social service environments since: (1) the process may not be visible; and (2) the relationship between intervention and outcome is not well-understood and/ir well-defined. Largely because of the complexity of social services, statistical quality control procedures have not generally been applied to improve process quality or to otherwise affect process outcomes. Because of these difficulties, the benefits of statistical quality control procedures and their ability to make processes more efficient and/or effective have largely been ignored in the literature on quality control. However, provided one can identify an objective outcome measure from a process (regardless of the complexity of that process), and make some assumptions about the prior distribution, acceptance sampling can be an appropriate and useful technique for monitoring process outcomes. Indeed, for many social processes, acceptance sampling, or testing “after the fact” may be the only approach available for monitoring shifts in process quality. We here demonstrate the utility of Bayesian acceptance sampling in the context of a timely social process, testing a population for the use of illegal drugs. The use of drugs, and the desire on the part of businesses and the criminal justice system to control or deter use has been a major focal point for policy and decision makers for more than a decade. Given that budgets to institute drug testing and/or screening programs are not unlimited, a technique that reduces the cost of a drug treatment program while maintaining deterrence and monitoring effects would indeed be useful to practitioners. We thus propose an application within the framework of an economic model of drug use, and show that adoption of the testing approach can reduce the expected cost of testing.