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INTRODUTIONThe types of methods for accident analysis and investigation that have developed since the 1990's reflect the increasing complexity of socio-technical systems, across a range of domains, including nuclear power production, rail and marine transportation (Waterson et al., 2015;2017). Socalled 'Systemic Accident Analysis' (SAA) draws on sociotechnical systems theory, (e.g., AccimapRasmussen, 1997) and control and resilience engineering (e.g., STAMP -Leveson, 2004; FRAMHollnagel, 2004) in order to illustrate the diversity of causal factors contributing to accident across different levels of the systems, interactions between them and the role played by external influences such as political, cultural, financial, and technological circumstances (Branford, 2011).
Systemic Accident Analysis (SAA): validity and reliabilityAlthough SAA are underpinned by sociotechnical systems theory, there are significant differences between them in terms of their theoretical assumptions, the type of causal representation and the outputs they produce. The extent to which methods for systemic accident analysis produce outcomes which are valid (e.g., the degree to which the Accimap and STAMP analysis successfully identifies the causes of an accident) and reliable (e.g., the degree to which accident analysts produce similar causal representations) are often viewed as an important criteria for judging their appropriateness for accident analysis (
Human Factors and Complex Systems Research Group, Loughborough Design School, Loughborough UniversityABSTRACT: The aim of this study was to assess the validity and reliability of two accident analysis methods. We identified four studies which analysed the same accident (the South Korea Sewol Ferry accident). Studies 1 and 2 used Accimap whilst Studies 3 and 4 applied STAMP. The four studies were compared in terms of analysis procedure taken, granularity of analysis, causal factors identified and recommendations suggested. The results indicate that the reliability between two STAMP studies (61%) is higher than two Accimap studies (31%) in terms of contributing factor identification. It was found that the recommendations made from each study reflect the focus and knowledge of the analyst.