Modern safety thinking and models focus more on systemic factors rather than simple cause-effect attributions of unfavourable events on the behaviour of individual system actors. This study concludes previous research during which we had traced practices of new safety thinking practices (NSTPs) in aviation investigation reports by using an analysis framework that includes nine relevant approaches and three safety model types mentioned in the literature. In this paper, we present the application of the framework to 277 aviation reports which were published between 1999 and 2016 and were randomly selected from the online repositories of five aviation authorities. The results suggested that all NSTPs were traceable across the sample, thus followed by investigators, but at different extents. We also observed a very low degree of using systemic accident models. Statistical tests revealed differences amongst the five investigation authorities in half of the analysis framework items and no significant variation of frequencies over time apart from the Safety-II aspect. Although the findings of this study cannot be generalised due to the non-representative sample used, it can be assumed that the so-called new safety thinking has been already attempted since decades and that recent efforts to communicate and foster the corresponding aspects through research and educational means have not yet yielded the expected impact. The framework used in this study can be applied to any industry sector by using larger samples as a means to investigate attitudes of investigators towards safety thinking practices and respective reasons regardless of any labelling of the former as “old” and “new”. Although NSTPs are in the direction of enabling fairer and more in-depth analyses, when considering the inevitable constraints of investigations, it is more important to understand the perceived strengths and weaknesses of each approach from the viewpoint of practitioners rather than demonstrating a judgmental approach in favour or not of any investigation practice.
Psychologists, psycholinguists, and other researchers using language stimuli have been struggling for more than 30 years with the problem of how to analyze experimental data that contain two crossed random effects (items and participants). The classical analysis of variance does not apply; alternatives have been proposed but have failed to catch on, and a statistically unsatisfactory procedure of using two approximations (known as F 1 and F 2) has become the standard. A simple and elegant solution using mixed model analysis has been available for 15 years, and recent improvements in statistical software have made mixed models analysis widely available. The aim of this article is to increase the use of mixed models by giving a concise practical introduction and by giving clear directions for undertaking the analysis in the most popular statistical packages. The article also introduces the djmixed add-on package for SPSS, which makes entering the models and reporting their results as straightforward as possible.
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Theme: Quality Assurance in Higher Education An online tool was developed for (potential) students to assess the congruence between the characteristics of an educational program and student preferences (Butter & Van Raalten, 2010)
