A model-based approach for the analysis of the calibration of probability judgments

The calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates...

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Main Authors: David V. Budescu, Timothy R. Johnson
Format: Article
Language:English
Published: Cambridge University Press 2011-12-01
Series:Judgment and Decision Making
Subjects:
Online Access:http://journal.sjdm.org/11/m01/m01.pdf
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author David V. Budescu
Timothy R. Johnson
author_facet David V. Budescu
Timothy R. Johnson
author_sort David V. Budescu
collection DOAJ
description The calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates conceptual problems, and may result in the confounding of explanatory variables or unstable estimates. To circumvent these problems we propose using probability estimates obtained from statistical models---specifically mixed models for binary data---in the analysis of calibration. We illustrate this methodology by re-analyzing data from a published study and comparing the results from this approach to those based on relative frequencies. The model-based estimates avoid problems with confounding variables and provided more precise estimates, resulting in better inferences.
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spelling doaj.art-8760c67a140f4197976b1a33174bbe512023-09-02T17:01:27ZengCambridge University PressJudgment and Decision Making1930-29752011-12-0168857869A model-based approach for the analysis of the calibration of probability judgmentsDavid V. BudescuTimothy R. JohnsonThe calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates conceptual problems, and may result in the confounding of explanatory variables or unstable estimates. To circumvent these problems we propose using probability estimates obtained from statistical models---specifically mixed models for binary data---in the analysis of calibration. We illustrate this methodology by re-analyzing data from a published study and comparing the results from this approach to those based on relative frequencies. The model-based estimates avoid problems with confounding variables and provided more precise estimates, resulting in better inferences.http://journal.sjdm.org/11/m01/m01.pdfcalibrationconfidence judgmentsmixed modelsmultilevel modelsoverconfidencesubjective probability.NAKeywords
spellingShingle David V. Budescu
Timothy R. Johnson
A model-based approach for the analysis of the calibration of probability judgments
Judgment and Decision Making
calibration
confidence judgments
mixed models
multilevel models
overconfidence
subjective probability.NAKeywords
title A model-based approach for the analysis of the calibration of probability judgments
title_full A model-based approach for the analysis of the calibration of probability judgments
title_fullStr A model-based approach for the analysis of the calibration of probability judgments
title_full_unstemmed A model-based approach for the analysis of the calibration of probability judgments
title_short A model-based approach for the analysis of the calibration of probability judgments
title_sort model based approach for the analysis of the calibration of probability judgments
topic calibration
confidence judgments
mixed models
multilevel models
overconfidence
subjective probability.NAKeywords
url http://journal.sjdm.org/11/m01/m01.pdf
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