Alternative solutions to the problem of selection bias in an analysis of federal residential drug treatment programs.
In an evaluation of prison-based residential drug treatment programs, the authors use three different regression-based approaches to estimating treatment effects. Two of the approaches, the instrumental variable and the Heckman approach, attempt to minimize selection bias as an explanation for treatment outcomes. Estimates from these approaches are compared with estimates from a regression in which treatment is represented by a dummy variable. The article discusses the advantage of using more than one method to increase confidence in findings when possible selection bias is a concern. Three-year outcome data for 2,315 federal inmates are used in analyses where the authors separately examine criminal recidivism and relapse to drug use for men and women. Statistical tests lead the authors to conclude that treatment reduces criminal recidivism and relapse to drug use. The treatment effect was largest when the inference was based on the Heckman approach, somewhat smaller when based on the instrumental variable approach, and smallest when based on the traditional dummy variable approach. Treatment effects for females were not statistically significant.
Rhodes
W
W
Pelissier
B
B
Gaes
G
G
Saylor
W
W
Camp
S
S
Wallace
S
S
D003415:Crime / Q000517:prevention & control / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011329:Prisoners / Q000706:statistics & numerical data*; D015397:Program Evaluation / Q000706:statistics & numerical data*; D015983:Selection Bias; D016320:Substance Abuse Treatment Centers / Q000706:statistics & numerical data*; D019966:Substance-Related Disorders / Q000534:rehabilitation*; D016019:Survival Analysis; D014481:United States