af Wåhlberg A E, Dorn L
Department of Psychology, Uppsala University, PO Box 1225, 751 42 Uppsala, Sweden.
J Safety Res. 2007;38(4):453-9. doi: 10.1016/j.jsr.2007.01.013. Epub 2007 Jul 26.
It is often implicitly or explicitly assumed in traffic accident research that drivers with accidents designated as non-culpable are a random sample from the population. However, this assumption is dependent upon differences in the criterion used for culpability. If drivers are erroneously categorized by assuming randomness, results could be grossly misleading.
The assumption of randomness leads to two predictions: first, no correlation should exist between culpable and non-culpable crashes; and second, the accident groups should differ on the variables known to be associated with accidents, such as amount of driving experience. These predictions were tested in two samples of bus drivers.
It was found that in a sample with a harsh criterion (70% culpable accidents) for crash responsibility, the drivers with non-culpable accidents had the features expected, namely, they were more experienced for example, while in a sample with a lenient criterion (50 % culpable), this was not so.
It was concluded that similar studies to the present one would need to be undertaken to establish exactly what percentage of drivers in a given population should be assigned culpable accidents, and construct a criterion that yields this ratio. Otherwise, the theoretical assumptions of randomness and non-responsibility will probably be violated to some degree.
Many estimates of risk of crash involvement may have been wrong. Given the potential for erroneous criteria, a number of studies may make invalid assumptions from their data.
在交通事故研究中,常常隐含或明确假定被认定为无过错的事故司机是总体中的随机样本。然而,这一假设取决于过错判定标准的差异。如果错误地基于随机性对司机进行分类,结果可能会极具误导性。
随机性假设产生两个预测:第一,有过错和无过错碰撞之间不应存在相关性;第二,事故组在已知与事故相关的变量上应存在差异,比如驾驶经验的多少。在两组公交司机样本中对这些预测进行了检验。
发现在一个对碰撞责任采用严格标准(70%有过错事故)的样本中,无过错事故的司机具有预期的特征,例如他们经验更丰富,而在一个采用宽松标准(50%有过错)的样本中则并非如此。
得出的结论是,需要开展与本研究类似的研究,以确切确定给定总体中应被认定为有过错事故的司机比例,并构建一个能得出该比例的标准。否则,随机性和无责任的理论假设可能会在某种程度上被违反。
许多对碰撞风险的估计可能有误。鉴于存在错误标准的可能性,一些研究可能会从其数据中做出无效假设。