The Predictive Ability Criterion in Experimental Judgment Research in Accounting*

Previous experimental judgment research in accounting has been interpreted as supportive of the linear model as an appropriate representation of decision‐making behavior in nearly all of the tasks investigated. Moreover, nonlinear models have come to be viewed as adding relatively little predictive power over that provided by the linear model, even in tasks considered to be inherently nonlinear. These conclusions were based largely upon evaluating the predictive ability of the linear model in terms of statistics measuring the proportion of variance accounted for by the model. In the present paper it is argued that, since these statistics are not independent of the experimental design, it is not clear whether the high correlations are indicative of the model's success in representing decision‐making processes or instead are more the result of various features of the experimental design. It is suggested that correlational tests be supplemented with qualitative tests of the predictive ability of a model. Implications for accounting are discussed.