The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were under- powered to detect the intervention effect, yet modeling the unreliability of the outcome measure increased their statistical power and helped in the detection of the hypothesized effect. Comparative Effectiveness Research (CER) could benefit from flexible multi- group alternative structural models organized in decision trees, and modeling unreliability of measures can be of tremendous help for both the fit of statistical models to the data and their statistical power.