Computational and mathematical models, in addition to providing a method for demonstrating qualitative predictions resulting from interacting mechanisms, provide quantitative predictions that can be used to discriminate between alternative models and uncover which aspects of a given theoretical framework require further elaboration. Unfortunately, there are no formal standards for how to evaluate the quantitative goodness-of-fit of models to data, either visually or numerically. As a result, there is considerable variability in methods used, with frequent selection of choices that misinform the reader. While there are some subtle and perhaps controversial issues involved in the evaluation of goodness-of-fit, there are many simple conventions that are quite uncontroversial and should be adopted now. We review various kinds of visual display techniques and numerical measures of goodness-of-fit, setting new standards for the selection and use of such displays and measures.

• Download Microsoft Excelfile, including:

• Example of how to compute various measures of goodness of fit
• Example of how to create a graph displaying goodness of fit

• Paper describing goodness of fits describing issues involving goodness of fit issues in Word format. Citation: Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press.

Last updated: 9/26/05

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