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On the Importance of Random Error in the Study of Probability Judgment. Part II: Applying the Stochastic Judgment Model to Detect Systematic Trends
Author(s) -
BUDESCU DAVID V.,
WALLSTEN THOMAS S.,
AU WING TUNG
Publication year - 1997
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/(sici)1099-0771(199709)10:3<173::aid-bdm261>3.0.co;2-6
Subject(s) - overconfidence effect , random error , replicate , artifact (error) , econometrics , systematic error , calibration , psychology , statistics , computer science , social psychology , mathematics , artificial intelligence
Erev, Wallsten, and Budescu (1994) and Budescu, Erev, and Wallsten (1997) demonstrated that over‐ and underconfidence often observed in judgment studies may be due, in part, to the presence of random error and its effects on the analysis of the judgments. To illustrate this fact they showed that a general model that assumes that overt responses representing (perfectly calibrated) true judgments perturbed by random error can replicate typical patterns observed in empirical studies. In this paper we provide a method for determining whether apparent overconfidence in empirical data reflects a systematic bias in judgment or is an artifact due solely to the presence of error. The approach is based, in part, on the Wallsten and González‐Vallejo (1994) Stochastic Judgment Model (SJM). The new method is described in detail and is used to analyze results from a new study. The analysis indicates a clear overconfidence effect, above and beyond the level predicted by a model assuming perfect calibration perturbed by random error. © 1997 John Wiley & Sons, Ltd.