Abstract
Effective training evaluation presents many challenges to the researcher and practitioner. We introduce rough sets theory and analysis as an analytic tool that can be employed to evaluate training systems effectively. The technique is especially helpful if any of the following situations exist in evaluating the training context: Data are discrete, relations among or between predictor and criterion variables are nonlinear, it is important to be able to determine the relative importance of predictor variables, or the concept describing the training criterion changes one or more times throughout the training pipeline. The technique is applied to data taken from a high-fidelity training simulator used by Air Force Airborne Warning and Control System (AWACS) teams. Both team- and position-level findings are presented. The discussion highlights the strengths and limitations of the approach.
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