One approach to predicting RTAs is to choose the variables that, when combined, have the highest predictive power. This can be best accomplished by a regression analysis, in this case a multiple regression technique. The problem with such analyses is that many variables are 'forced out' of the equation because they fail to achieve significance when placed along side stronger predictors despite their significant correlation with the dependant variable. This type of analyses is perfectly justifiable as long as one realises that causal relationships cannot be determined definitely from correlational data, and if the only aim of the study is to achieve the best prediction possible. However, such an approach is limited insofar as it does not necessarily allow one to explore the role played by each of the variables. The aim of this study is to develop a Structural Equation Model (SEqM) which not only specifies which variables and factors are good predictors of RTAs but one in which the structural relationship between all of the variables and factors would be made explicit.
Another reason for using a structural approach could be if some future intervention was planned with the aim of reducing RTAs. In this case, it may be possible to identify a variable that could be more amenable to modification compared to the other variables, has a profound influence on RTAs, but is removed from a multiple regression analysis when placed in competition with other variables that are not so amenable and are more powerful.
In addition, a structural approach may indicate the presence of latent variables, which have yet to be defined by future research. For example, if it was necessary to include a direct path between age and passive RTA's, then the presence of some other factor would be likely as age, in a vacuum, tells us very little. Therefore, we could probably assume that the presence of unaccounted cognitive co-variables, such as selective attention and spatial memory, are playing an active role in passive accident involvement. Finally, a SEqM may also be used to test the viability of alternative models.
The SEqM is a relatively new approach within the context of RTA analysis. However, it has demonstrated that it can effectively be used to show the interaction of critical factors with great clarity. For example, a study by Rimmo & Åberg (1999) used a number of SEqMs to show the distinction between driving violations and errors, concerning sensation seeking. The use of this hierarchical approach also enabled them to identify a further causal relationship between sensation seeking and aberrant driving behaviour.
Type A Behaviour Pattern (TABP) has been chosen as the main personality factor to be measured as it is characterised by impatience, aggression, and competitive achievement orientation. It is therefore expected that TABP will be manifested in the driving situation mainly through increased risk taking and a disregard for traffic regulations. It is also hypothesised that TABP will be linked to demographic variables such as age, sex, and preference for performance vehicles. Other individual differences to be explored are trait-anxiety, cognitive slips and lapses, aberrant driving behaviour, and sensation seeking. All of which have proven reliable measures of RTA causation by themselves.
The Cross-Cultural Perspective:
This study was to be conducted in two parts. The first part having been directed at drivers within the United Kingdom. Whilst the second part would explore whether or not the structural equation model gleaned from UK data needed to be modified within diverse European cultures, namely Finland and Italy.
There has been research to support the assumption that driver behaviour does indeed differ between European states. For example, Marsh & Collett (1986) reported a study by Forgas (1986) in which the author drove a Volkswagen through four European countries sporting an Australian registration plate. The Author discovered that the French, Spanish and Italian drivers were quicker to beep their horns at the VW when it failed to move at a clear junction. In comparison, it was found that the Germans were the least likely to use their horns. In the study, the Italians were the quickest to use their horns.
The maintenance of fixed individual distance varies greatly in different human societies. Distances that may be considered as invasions of personal space depend very much on the individual’s culture and can easily lead to misunderstandings. In Southern Europe people interact at close range, e.g. kissing when greeting and saying goodbye, and frequently touching during conversation. However, this is found to be very disconcerting in the UK, and contact is usually kept to a minimum, (Hall, 1966). This propensity may well extend into the driving environment. Increased likelihood of RTAs in Northern European countries through altercations brought about through ‘tailgating’ may occur. However, in Southern European countries, such as Italy, close driving is the norm and therefore less likely to elicit hostility between drivers. Nevertheless, this close following behaviour is more likely to increase accident risk through reduced stopping distances. This is one of a number of reasons why the questionnaire was to be distributed equally in The U.K., Finland and Italy.
Moreover, official government statistics (International Road Traffic & Accident Database (OECD), Issue: May 2000) has shown stark differences in RTA fatalities and injuries within the European states. For example, In 1998 there were 25 motorcycle related deaths and 232 car related deaths in Finland. In the U.K. there were 509 motorcycle related deaths and 1789 car related deaths. However, in Italy there were 1193 motorcycle related deaths and 3522 car related deaths. It appears, at first glance, the further south one travels in Europe, the greater the risk of RTA. However, if the statistics are considered per 100,000 population we see an altogether different picture, i.e. Italy still has the highest fatality rate with 11.0 killed per 100,000 population. However, Finland becomes second highest with 7.8 killed per 100,000 population in comparison with the U.K.'s lowest of 6.0 killed per 100,000 population. On the otherhand, RTA injury accidents per 100,000 population show the U.k. as being the highest at 415 injured per 100,000 population, followed by Italy and Finland with 355 and 134 per 100,000 population respectively. Despite these statistics, the researchers await data that will indicate reported RTAs were there has just been damage to vehicle(s) (regardless of fatality or injury).
Cross-cultural research has consistently found a causal link between many of the factors included in this study. For example, TABP and RTA risk - the differences in the expression of TABP traits between PSV drivers in North America and India, (Evans, Palsane & Carrere, 1987); and between drivers in Finland and Australia, (Lajunen et al., 1998). Nevertheless, there is a history of poor ecological validity, i.e. small sample sizes; lack of diversity between the occupations of the participants; little or no emphasis on participant sex; over generalisation on a global scale; the possible psychometric variance arising from translating tests into another language; and too much diversity between participants nationalities. This study aimed to address these discrepancies.
Data Collection:
Each group of drivers sampled would comprise of approximately 400-500 respondents. Data would be collated using a structured self-report questionnaire. This will be delivered by a number of means, e.g. postage, by hand, and by placing on car windscreens in car parks. The questionnaires would be returned in a pre-paid SAE.
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