The DOI matches its Crossref record.
METHODS FOR DISCRIMINATING LEVELS OF PARTIAL KNOWLEDGE CONCERNING A TEST ITEM
Abstract
It is argued that where a person is uncertain as to the true answer in a multiple choice question, he should be encouraged to express his partial knowledge in terms of the subjective probability he attaches to each alternative being correct. A variety of answering techniques are examined, together with ways of scoring them, to determine how far they provide an adequate appraisal of the subjective probabilities. Ways of choosing an appropriate method and of improving it are considered and it is suggested that, by suitable training, subjects may be provided with a better understanding of the proper basis for making sound decisions.
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Citations
Cited by
Fraassen, B. C. V. (1989) · doi:10.1093/0198248601.001.0001 · Cited by 1,821
Traders' Expectations in Asset Markets: Experimental Evidence
Haruvy, E., Lahav, Y., & Noussair, C. N. (2007) · American Economic Review · doi:10.1257/aer.97.5.1901 · Cited by 398
Metadata
- Authors
- Finetti, B. D.
- Venue
- British Journal of Mathematical and Statistical Psychology
- Publisher
- Wiley
- Published
- 1965-05
- Volume
- 18
- Issue
- 1
- Pages
- 87-123
- ISSN
- 0007-1102 (ISSN-L)
- 2044-8317
- OpenAlex
- W1991156325
- Cited by
- 190
- References
- 0
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: METHODS FOR DISCRIMINATING LEVELS OF PARTIAL KNOWLEDGE CONCERNING A TEST ITEM. https://app.cochat.ai/papers/10.1111/j.2044-8317.1965.tb00695.x
- The paper
- Finetti, B. D. (1965). METHODS FOR DISCRIMINATING LEVELS OF PARTIAL KNOWLEDGE CONCERNING A TEST ITEM. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/j.2044-8317.1965.tb00695.x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
