Abstract
This paper is concerned with the types of models researchers use to analyze empirical data in the domain of social judgments and decisions. Models for the analysis of judgment data may be divided into two classes depending on the criteria they optimize: Optimizing an internal (mathematical) criterion function with the aim to minimize the discrepancy of values predicted by the model from obtained data or incorporating a substantive underlying theory into the model where model parameters are not only formally defined, but represent specified components of judgments. Results from applying models from both classes to empirical data exhibit considerable differences between the models in construct validity, but not in empirical validity. It may be concluded that any model for the analysis of judgment data implies the selection of a formal theory about judgments. Hence, optimizing a mathematical criterion function does not induce a non-theoretical rationale or neutral tool. As a consequence, models satisfying construct validity seem superior in the domain of judgments and decisions.
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Krolak-Schwerdt, S. (2009). Strategies of Model Construction for the Analysis of Judgment Data. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_2
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DOI: https://doi.org/10.1007/978-3-642-01044-6_2
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