Comparing Regression Approaches in Modelling Compensatory and Noncompensatory Judgment Formation

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Applied research on judgment formation, e.g. in education, is interested in identifying the underlying judgment rules from empirical judgment data. Psychological theories and empirical results on human judgment formation support the assumption of compensatory strategies, e.g. (weighted) linear models, as well as noncompensatory (heuristic) strategies as underlying judgment rules. Previous research repeatedly demonstrated that linear regression models well fitted empirical judgment data, leading to the conclusion that the underlying cognitive judgment rules were also linear and compensatory. This simulation study investigated whether a good fit of a linear regression model is a valid indicator of a compensatory cognitive judgment formation process. Simulated judgment data sets with underlying compensatory and noncompensatory judgment rules were generated to reflect typical judgment data from applied educational research. Results indicated that linear regression models well fitted even judgment data with underlying noncompensatory judgment rules, thus impairing the validity of the fit of the linear model as an indicator of compensatory cognitive judgment processes.



The preparation of this paper was supported by AFR PhD grant 2962244 of the Luxembourgish Fond Nationale de la Recherche (FNR). Grade distribution data were provided by the FNR-CORE-project TRANSEC (C08/LM/02).


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of LuxembourgWalferdangeLuxembourg

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