Publications

Recent papers have argued for the benefit of a tighter integration of the disciplines of human factors (HF) and human reliability analysis (HRA). While both disciplines are concerned with human performance, HF uses performance data to prescribe optimal human-machine interface (HMI) design, while HRA applies human performance principles and data to model the probabilistic risk of human activities. An overlap between the two disciplines is hindered by the seeming incompatibility of their respective data needs. For example, while HF studies produce data especially about the efficacy of particular system designs, these efficacy data are rarely framed in such a way as to provide the magnitude of the performance effect in terms of human error. While qualitative insights for HRA result from the HF studies, the HF studies often fail to produce data that inform the quantification of human error. In this paper, the author presents a review of the data requirements for HRA and offers suggestions on how to piggyback HRA data collection on existing HF studies. HRA data requirements include specific parameters such as the effect size of the human performance increment or degradation observed and classification of the human performance according to a simple set of performance shaping factors.