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Predicting pilot error: Testing a new methodology and a multi-methods and analysts approach

Stanton, Neville A. (author)
Salmon, Paul (author)
Harris, Don (author)
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Marshall, Andrew (author)
Demagalski, Jason (author)
Young, Mark S. (author)
Waldmann, Thomas (author)
Dekker, Sidney (author)
Lund University,Lunds universitet,Trafikflyghögskolan,Institutioner vid LTH,Lunds Tekniska Högskola,School of Aviation,Departments at LTH,Faculty of Engineering, LTH
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 (creator_code:org_t)
Elsevier BV, 2009
2009
English.
In: Applied Ergonomics. - : Elsevier BV. - 1872-9126 .- 0003-6870. ; 40:3, s. 464-471
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • The Human Error Template (HET) is a recently developed methodology for predicting design-induced pilot error. This article describes a validation study undertaken to compare the performance of HET against three contemporary Human Error Identification (HEI) approaches when used to predict pilot errors for an approach and landing task and also to compare analyst error predictions to an approach to enhancing error prediction sensitivity: the multiple analysts and methods approach, whereby multiple analyst predictions using a range of HEI techniques are pooled. The findings indicate that, of the four methodologies used in isolation, analysts using the HET methodology offered the most accurate error predictions, and also that the multiple analysts and methods approach was more successful overall in terms of error prediction sensitivity than the three other methods but not the HET approach. The results suggest that when predicting design-induced error, it is appropriate to use a toolkit of different HEI approaches and multiple analysts in order to heighten error prediction sensitivity. (c) 2008 Elsevier Ltd. All rights reserved.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)

Keyword

and validity
Reliability
Error prediction
Human error
Human Error Identification

Publication and Content Type

art (subject category)
ref (subject category)

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