Learning to predict human error: issues of reliability, validity and acceptability
Learning to predict human error: issues of reliability, validity and acceptability
Human Error Identification (HEI) techniques have been used to predict human error in high risk environments for the past two decades. Despite the lack of supportive evidence for their efficacy, their popularity remains unabated. The application of these approaches is ever-increasing, to include product assessment. The authors feel that it is necessary to prove that the predictions are both reliable and valid before the approaches can be recommended with any confidence. This paper provides evidence to suggest that human error identification techniques in general, and SHERPA in particular, may be acquired with relative ease and can provide reasonable error predictions.
1737-1756
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Stevenage, Sarah V.
493f8c57-9af9-4783-b189-e06b8e958460
November 1998
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Stevenage, Sarah V.
493f8c57-9af9-4783-b189-e06b8e958460
Stanton, Neville A. and Stevenage, Sarah V.
(1998)
Learning to predict human error: issues of reliability, validity and acceptability.
Ergonomics, 41 (11), .
(doi:10.1080/001401398186162).
(PMID:9819584)
Abstract
Human Error Identification (HEI) techniques have been used to predict human error in high risk environments for the past two decades. Despite the lack of supportive evidence for their efficacy, their popularity remains unabated. The application of these approaches is ever-increasing, to include product assessment. The authors feel that it is necessary to prove that the predictions are both reliable and valid before the approaches can be recommended with any confidence. This paper provides evidence to suggest that human error identification techniques in general, and SHERPA in particular, may be acquired with relative ease and can provide reasonable error predictions.
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Published date: November 1998
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Local EPrints ID: 76095
URI: http://eprints.soton.ac.uk/id/eprint/76095
ISSN: 1366-5847
PURE UUID: af2625ff-6638-4514-8e5e-59b680a26ee8
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Date deposited: 11 Mar 2010
Last modified: 14 Mar 2024 02:54
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