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Quantitative scores for binary qualitative proficiency testing

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Quantitative scores for binary qualitative proficiency testing. / Beavis, Guy; Wilson, Julie Carol; Sykes, Mark.

In: Accreditation and Quality Assurance , Vol. 24, No. 4, 08.2019, p. 263-269.

Research output: Contribution to journal › Article

Harvard

Beavis, G, Wilson, JC & Sykes, M 2019, 'Quantitative scores for binary qualitative proficiency testing', Accreditation and Quality Assurance , vol. 24, no. 4, pp. 263-269. https://doi.org/10.1007/s00769-019-01386-8

APA

Beavis, G., Wilson, J. C., & Sykes, M. (2019). Quantitative scores for binary qualitative proficiency testing. Accreditation and Quality Assurance , 24(4), 263-269. https://doi.org/10.1007/s00769-019-01386-8

Vancouver

Beavis G, Wilson JC, Sykes M. Quantitative scores for binary qualitative proficiency testing. Accreditation and Quality Assurance . 2019 Aug;24(4):263-269. https://doi.org/10.1007/s00769-019-01386-8

Author

Beavis, Guy ; Wilson, Julie Carol ; Sykes, Mark. / Quantitative scores for binary qualitative proficiency testing. In: Accreditation and Quality Assurance . 2019 ; Vol. 24, No. 4. pp. 263-269.

Bibtex - Download

@article{ce6d8d7461874087922e5ffd7a5b17cf,
title = "Quantitative scores for binary qualitative proficiency testing",
abstract = "While z-scores provide participants with easy-to-interpret scores for quantitative proficiency tests, there is no universally accepted equivalent scoring method available for qualitative testing. Under the assumption that these tests follow a binomial distribution, it is possible to calculate scores that mimic the widely used z-scores and provide participants with insight into their performance level. We show that these scores, which we term a-scores, can be combined to provide a single score for multiple tests so that participants can monitor their performance over time, and discuss the use of the exact binomial test in place of uncertainty when there is no clear consensus.",
keywords = "Proficiency testing, Quantitative assessments, a-scores, z-scores",
author = "Guy Beavis and Wilson, {Julie Carol} and Mark Sykes",
note = "{\circledC} Springer-Verlag GmbH Germany, part of Springer Nature 2019. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2019",
month = "8",
doi = "10.1007/s00769-019-01386-8",
language = "English",
volume = "24",
pages = "263--269",
journal = "Accreditation and Quality Assurance",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Quantitative scores for binary qualitative proficiency testing

AU - Beavis, Guy

AU - Wilson, Julie Carol

AU - Sykes, Mark

N1 - © Springer-Verlag GmbH Germany, part of Springer Nature 2019. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2019/8

Y1 - 2019/8

N2 - While z-scores provide participants with easy-to-interpret scores for quantitative proficiency tests, there is no universally accepted equivalent scoring method available for qualitative testing. Under the assumption that these tests follow a binomial distribution, it is possible to calculate scores that mimic the widely used z-scores and provide participants with insight into their performance level. We show that these scores, which we term a-scores, can be combined to provide a single score for multiple tests so that participants can monitor their performance over time, and discuss the use of the exact binomial test in place of uncertainty when there is no clear consensus.

AB - While z-scores provide participants with easy-to-interpret scores for quantitative proficiency tests, there is no universally accepted equivalent scoring method available for qualitative testing. Under the assumption that these tests follow a binomial distribution, it is possible to calculate scores that mimic the widely used z-scores and provide participants with insight into their performance level. We show that these scores, which we term a-scores, can be combined to provide a single score for multiple tests so that participants can monitor their performance over time, and discuss the use of the exact binomial test in place of uncertainty when there is no clear consensus.

KW - Proficiency testing

KW - Quantitative assessments

KW - a-scores

KW - z-scores

UR - http://www.scopus.com/inward/record.url?scp=85064817388&partnerID=8YFLogxK

U2 - 10.1007/s00769-019-01386-8

DO - 10.1007/s00769-019-01386-8

M3 - Article

VL - 24

SP - 263

EP - 269

JO - Accreditation and Quality Assurance

T2 - Accreditation and Quality Assurance

JF - Accreditation and Quality Assurance

IS - 4

ER -

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