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Validating a forced-choice method for eliciting quality-of-reasoning judgments
Alexandru Marcoci1, Margaret E Webb2, Luke Rowe3
1Centre for the Study of Existential Risk, University of Cambridge, 16 Mill Lane, Cambridge, CB2 1SB, UK. am3159@cam.ac.uk.
Behavior Research Methods
|October 13, 2023
Summary
Forced-choice comparisons effectively assess written argument quality, with novices and experts accurately identifying better reasoning. This method offers a valid, reliable, and efficient approach for large-scale quality assessments.
Area of Science:
- Cognitive Psychology
- Educational Measurement
- Artificial Intelligence
Background:
- Evaluating the quality of written arguments is crucial but often subjective.
- Traditional methods can be time-consuming and require expert raters.
- Developing scalable and reliable assessment tools is an ongoing challenge.
Purpose of the Study:
- To investigate the criterion validity of forced-choice comparisons for assessing argument quality.
- To determine the reliability and efficiency of this assessment method for novices and experts.
- To explore methods for optimizing the assessment process and utilizing linguistic features.
Main Methods:
- Two studies were conducted using a forced-choice design where participants compared arguments.
- Inter-rater reliability was calculated to assess agreement between raters.
- Efficiency was improved using transitivity and an AVL tree method; a regression model analyzed linguistic features.
Main Results:
- Novices and experts accurately identified arguments supporting more accurate solutions (62.2% and 74.4% respectively).
- Participants correctly identified arguments from larger teams (82% for novices, 85% for experts) with high inter-rater reliability.
- Efficiency methods reduced judgment numbers with minimal accuracy loss; a regression model showed high correlation with objective scores.
Conclusions:
- Forced-choice comparisons provide a valid and reliable method for assessing reasoning quality, even for novices.
- The method is efficient and scalable for large-volume assessments.
- Leveraging transitivity and linguistic analysis offers further optimization for argument quality evaluation.
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