Highlights MDSI was adapted to the language, culture, norms and traffic habits of Spanish drivers. Cluster analysis identified two profiles: Adaptive (Careful) DS vs. Maladaptive (Reckless, Anxious, Angry and Dissociative) DS. Correlations were found between the Maladaptive DS or the Careful DS (positive or negative, respectively) and DBQ sub-scales. Positive correlations were found between the Reckless DS and Sensitivity to Reward. Drivers diagnosed with Alcohol Dependence Syndrome had higher scores on Maladaptive DS.

    Abstract The Multidimensional Driving Style Inventory (MDSI; Taubman – Ben-Ari et al., 2004) is a well-known and useful instrument that allows us to identify not only “maladaptive” Driving Styles (DS) in order to modify them, but also “adaptive” DS to encourage safe driving. The aim of this study was to adapt the MDSI to the Spanish spoken in Spain and to the rules and driving habits of Spaniards. The Argentinian version of the MDSI was taken as the source version. The sample consisted of 1173 drivers, who completed the Spanish version of the MDSI. The factor structure was analysed by means of an Exploratory Factor Analysis (AFE) and a Confirmatory Factor Analysis (AFC). The 6-factor structure of the Argentinian version of the MDSI was replicated with higher internal consistency values for each of the DS. The original Argentinian and the Spanish versions share 23 items, indicating a relevant overlap in the construct. A cluster analysis grouped the DS into two groups: maladaptive and adaptive. Significant associations were found between DS measures and demographic variables (gender, age, and education level), driving history and theoretically related constructs like the Domain-Specific Risk-Taking Scale (DOSPERT); Lapses, Errors, Violations; Angry Driving; and Sensitivity to Rewards. The Spanish MDSI provides valid measures that could help us understand complex driving behaviours and promote safe driving.


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    Titel :

    Adaptation of the multidimensional driving styles inventory for Spanish drivers: Convergent and predictive validity evidence for detecting safe and unsafe driving styles


    Beteiligte:


    Erscheinungsdatum :

    2019-12-23




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch