Browsing by Author "Kline, T."
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Item Open Access The effect of social desirability on self reported and recorded road traffic accidents(Elsevier Science B.V., Amsterdam., 2010-03-31T00:00:00Z) af Wåhlberg, Anders E.; Dorn, Lisa; Kline, T.The use of lie scales has a fairly long history in psychometrics, with the intention of identifying and correcting for socially desirable answers. This represents one type of common method variance (bias introduced when both predictors and predicted variables are gathered from the same source), which may lead to spurious associations in self-reports. Within traffic safety research, where self-report methods are used abundantly, it is uncommon to control for social desirability artifacts, or reporting associations between lie scales, crashes and driver behaviour scales. In the present study, it was shown that self-reports of traffic accidents were negatively associated with a lie scale for driving, while recorded ones were not, as could be expected if the scale was valid and a self-report bias existed. We conclude that whenever self-reported crashes are used as an outcome variable and predicted by other self-report measures, a lie scale should be included and used for correcting the associations. However, the only existing lie scale for traffic safety is not likely to catch all socially desirable responding, because traffic safety may not be desirable for all demographic groups. New lie scales should be developed specifically for driver behaviour questionnaires, to counter potential bias and artifactual results. Alternatively, the use of a single source of data should be discontinued. (C) 2009 Elsevier Ltd. All rights reserved.Item Open Access The Manchester Driver Behaviour Questionnaire as predictor of road traffic accidents(Taylor & Francis, 2011-02-28T00:00:00Z) af Wåhlberg, Anders E.; Dorn, Lisa; Kline, T.The Driver Behaviour Questionnaire (DBQ) has mainly been used as predictor of self-reported road traffic accidents. The associations between crashes and the violation and error factors of the DBQ however, may be spuriously high due to reporting bias. In the present study, the DBQ was tested as predictor of self- reported and recorded accidents in four samples of private and professional drivers. The findings show that the DBQ scale only predicts self-reported accidents, not recorded crashes, despite the higher validity of company data, and the higher means of the recorded data across these samples. The results can be explained by a common method variance bias. In our review of the DBQ research, the use of the instrument was found to be heterogeneous concerning the number of items, scales used, and factor analytic methods applied. Thus, the DBQ may not be as homogenous and as successful in predicting accidents as is often claimed.