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Learning to predict human error: issues of reliability, validity and acceptability

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.
1366-5847
1737-1756
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Stevenage, Sarah V.
493f8c57-9af9-4783-b189-e06b8e958460
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), 1737-1756. (doi:10.1080/001401398186162). (PMID:9819584)

Record type: Article

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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More information

Published date: November 1998

Identifiers

Local EPrints ID: 76095
URI: http://eprints.soton.ac.uk/id/eprint/76095
ISSN: 1366-5847
PURE UUID: af2625ff-6638-4514-8e5e-59b680a26ee8
ORCID for Neville A. Stanton: ORCID iD orcid.org/0000-0002-8562-3279
ORCID for Sarah V. Stevenage: ORCID iD orcid.org/0000-0003-4155-2939

Catalogue record

Date deposited: 11 Mar 2010
Last modified: 14 Mar 2024 02:54

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