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Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms.

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Title: Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms.
Authors: Befani, Barbara1 (AUTHOR) b.befani@surrey.ac.uk, D'Errico, Stefano1 (AUTHOR)
Source: New Directions for Evaluation. Fall2020, Vol. 2020 Issue 167, p27-43. 17p.
Subject Terms: *Confidence, Cognitive bias, Proof of God, Evidence, Consumer expertise
Abstract: This chapter argues that the credibility of causal mechanisms can be greatly increased by formulating them as statements that are both empirically falsifiable and empirically confirmable. Whether statements can be so depends on the potential availability of the relevant evidence (e.g., no evidence exists that can prove or disprove the existence of God, but good quality evidence is potentially available in many other cases). The Bayes formula can be used to measure the extent to which a given set of empirical observations supports or weakens the belief that a causal mechanism exists. With this approach, confidence in the existence of a mechanism is increased or decreased through a process that can be open, transparent, and shared with the public or groups of stakeholders, reducing cognitive biases, and improving internal validity and consensus around the existence of given mechanisms. The approach is showcased in the evaluation of a learning partnership whereby a knowledge product released by a research organization influenced policy at the municipal level. [ABSTRACT FROM AUTHOR]
Copyright of New Directions for Evaluation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms.
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  Data: <searchLink fieldCode="AR" term="%22Befani%2C+Barbara%22">Befani, Barbara</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> b.befani@surrey.ac.uk</i><br /><searchLink fieldCode="AR" term="%22D'Errico%2C+Stefano%22">D'Errico, Stefano</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22New+Directions+for+Evaluation%22">New Directions for Evaluation</searchLink>. Fall2020, Vol. 2020 Issue 167, p27-43. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Confidence%22">Confidence</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+bias%22">Cognitive bias</searchLink><br /><searchLink fieldCode="DE" term="%22Proof+of+God%22">Proof of God</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence%22">Evidence</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+expertise%22">Consumer expertise</searchLink>
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  Data: This chapter argues that the credibility of causal mechanisms can be greatly increased by formulating them as statements that are both empirically falsifiable and empirically confirmable. Whether statements can be so depends on the potential availability of the relevant evidence (e.g., no evidence exists that can prove or disprove the existence of God, but good quality evidence is potentially available in many other cases). The Bayes formula can be used to measure the extent to which a given set of empirical observations supports or weakens the belief that a causal mechanism exists. With this approach, confidence in the existence of a mechanism is increased or decreased through a process that can be open, transparent, and shared with the public or groups of stakeholders, reducing cognitive biases, and improving internal validity and consensus around the existence of given mechanisms. The approach is showcased in the evaluation of a learning partnership whereby a knowledge product released by a research organization influenced policy at the municipal level. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of New Directions for Evaluation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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