The acceptance and accuracy of decision analysis methods

This paper reports a field experiment which compared two approaches to decision analysis, called cases and criteria scaling, in terms of their acceptance to users and their predictive features. The case method simulates decisions likely to occur in practice. Each decision is described by particular values of the decision criteria. Experts consider the values of criteria present in each decision and make a judgment. Regression is used to correlate the criteria values with the judgments. The regression equation provides the prediction model. The criteria scaling method decomposes the decision task. Experts weight each criterion and specify how decisions change across levels of each criterion. The predictive model is made up of the sum of each criterion weight multiplied by the criterion-predictor functional relationship. Both methods were applied to build models which predicted the demands for nursing time, using patient severity indicators, for two hospital units. The case method had considerable predictive accuracy and had favorable participant reactions. Predictions made by criteria scaling overestimated needs and this method was viewed as inaccurate and hard to understand by participants. The implications of these findings are discussed.