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Updated: May 1, 2026

Design and Analysis for Fall Detection System Simplification
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Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

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Human error data collection and comparison with predictions by SPAR-H.

Peng Liu1, Zhizhong Li

  • 1Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|April 3, 2014
PubMed
Summary

This study addresses the lack of human error data in human reliability analysis (HRA). Empirical findings reveal performance shaping factors significantly impact human error probability, with existing models showing optimistic bias.

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Area of Science:

  • Risk analysis and human reliability analysis (HRA).
  • Human factors engineering and cognitive psychology.

Background:

  • Empirical data on human error is scarce, impacting the validity of human reliability analysis (HRA) in risk assessment.
  • Existing HRA models, such as the Standardized Plant Analysis Risk-Human Reliability Method (SPAR-H), may produce optimistic predictions due to limited empirical validation.

Purpose of the Study:

  • To investigate the effects of performance shaping factors (PSFs) on human error probability (HEP) using a microworld study.
  • To compare empirical HEP data with SPAR-H predictions and other existing data sources.
  • To analyze the interrelationships and combined effects of PSFs on human error.

Main Methods:

  • A controlled microworld study was conducted to collect empirical data on human error.
Keywords:
Human error datahuman reliability analysisperformance shaping factors

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Last Updated: May 1, 2026

Design and Analysis for Fall Detection System Simplification
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  • Performance shaping factors (PSFs) investigated included task complexity, time availability, experience, and time pressure.
  • Empirical data on HEP, relative PSF effects, and error types were compared against SPAR-H predictions and other datasets.

    • Main Results:

    • Human error probability (HEP) decreased with increased experience and time availability.
    • The relationship between task complexity and HEP was moderated by time availability and experience.
    • Time availability influenced HEP by affecting time pressure.
    • Empirical HEPs were consistently higher than SPAR-H predictions, indicating a tendency for SPAR-H to be optimistic.
    • Relative effects of experience/training and stress/stressors in SPAR-H showed partial agreement with empirical findings.
    • Identified error types aligned well with those from nuclear power plant operational experience and databases.

    Conclusions:

    • Empirical data from microworld studies are crucial for improving the accuracy and validity of HRA in risk analysis.
    • The SPAR-H model may overestimate human reliability, suggesting a need for recalibration with empirical data.
    • Understanding the complex interplay of PSFs is essential for accurate human error prediction and risk assessment.