The Manchester Driver Behaviour Questionnaire: a cross-cultural study

BackgroundThe Driver Behavior Questionnaire (DBQ) is one of the most used tools for measuring self-reported aberrant driving behavior. Several versions of the DBQ exist varying in terms of the item number and factor structure.ObjectivesThis study aimed to investigate the psychometric properties and the factorial structure of the DBQ for assessing aberrant behavior among Lebanese drivers and to examine the relationship between DBQ subscales and socio-demographic characteristics and traffic-related variables.MethodsA cross-sectional study was conducted among Lebanese drivers aged over 18 years from all Lebanese governorates. Data were collected using a convenient sampling technique and an anonymous Arabic self-administered questionnaire that included socio-demographic characteristics and the Arabic Lebanese version of the DBQ (DBQ-L). Statistical analyses were performed using SPSS version 24 and Amos software. Exploratory factor analysis (N = 568) and confirmatory factor analysis (N = 534) were used to test the factorial structure of the DBQ-L. Linear regression analyses were performed to examine the association between DBQ-L, sociodemographic, and relevant traffic variables.ResultsA total of 1102 drivers aged between 18 and 82 years participated in the study with an average age of 34.64 (SD = 12.33). Of the total, 68.4% of participants were male with an average driving experience of 13.46 years (SD = 10.76). The DBQ-L total scale revealed high internal consistency (Cronbach’s α = 0.892). Exploratory and confirmatory factor analysis supported the original four-factor structure reflecting aggressive violations, ordinary violations, errors, and lapses dimensions. Aggressive violations had the highest mean among all the DBQ subscales. Men were found to have a higher tendency to commit aggressive and ordinary violations than females. Furthermore, the ordinary violation score was positively associated with younger age and increased mileage. All DBQ subscales decreased with the highest educational level. Being a professional driver increased the likelihood of drivers reporting violations, errors, and lapses, and increased mileage amplified the tendency of displaying aggressive violations and lapses when driving. However, errors decreased for young drivers and increased with age and larger driver experience.ConclusionThe cross-cultural adapted version of the DBQ was found to be a reliable and valid tool for assessing aberrant behaviors among Lebanese drivers. This study revealed distinct behavior patterns, with men frequently displaying aggressive violations, and younger, high-mileage drivers more prone to ordinary violations and lapses. Higher education correlated with safer driving, while professional drivers reported more violations, errors, and lapses. These findings highlight the importance of tailored road safety interventions based on driver demographics. Further studies are needed to examine the ability of DBQ to predict road accidents.

Age, gender, mileage and the DBQ: The validity of the Driver Behavior Questionnaire in different driver groups

French validation of a new version of the Driver Behavior Questionnaire (DBQ) for drivers of all ages and level of experiences

Although there have been several attempts at improving the COPE Inventory, the factor structure of the instrument is still in dispute. In addition, studies have shown low reliability coefficients for some of the first-order factors, with Mental Disengagement having the lowest factor loadings. In a recent study on the external validation of the instrument, two additional first-order factors were identified in the qualitative analysis, namely Self-care and Care for Others. Based on these arguments we created the Revised COPE 68 Inventory, changing some of the problematic items in the first order factor Mental Disengagement and adding items for the two new factors (Self-care and Care for Others). We then tested its reliability and performed factor analyses on the first and second-order factorial structure. The data were collected through social media in two languages, English and Slovak, using convenience and snowball sampling techniques. The English sample contained 834 participants with mean age 25.27 years (SD = 8.467) and the Slovak sample comprised 1,425 participants with mean age 33 years (SD = 14.59). For the statistical analyses we used Exploratory Structural Equation Model (ESEM) analyses with target rotation and WLSMV, Exploratory and second-order confirmatory factor analysis with the scores of the COPE Inventory and EFA. The Revised COPE 68 inventory had a good fit for all 17 first-order factors in both languages, including for the new factors Self-care and Care for Others. It appears that the first-order factors form a three-factor solution in both samples, consisting of active coping, social-emotional coping and avoidant coping. The revised Mental Disengagement has better psychometric properties as well. The Revised COPE 68 inventory was found to be a reliable multidimensional instrument for measuring various coping strategies in both the English and Slovak language versions.

Conceptualisation of service quality for hybrid services: a hierarchical approach

Schizotypy refers to traits similar to schizophrenia symptoms and is related to cluster A personality disorders. Previous factor analytic studies have found a positive schizotypy factor distinct from a negative factor. However, some evidence suggests that the positive factor may itself be multidimensional, but the factor structure of positive schizotypy is still unclear. The current study provided converging evidence through four different analyses that positive schizotypy is multidimensional. First, a factor model with three positive schizotypy factors (paranoia, referential thinking, and cognitive-perceptual) fit the data better than models with fewer than three factors. Second, a factor model with a second-order (i.e., higher-order) positive schizotypy factor fit the data significantly worse than a factor model without a second-order factor in which first-order factors were allowed to correlate freely, suggesting that the second-order factor does not completely account for relations among the first-order factors. Third, a Schmid-Leiman transformation found that even after accounting for the second-order factor that meaningful variance was attributed to the first-order factors. Finally, the three positive schizotypy factors displayed differential relations with five-factor model personality traits. Overall, results suggest that positive schizotypy is composed of correlated but distinct factors.

Following a driving simulator experiment investigating anticipatory competence in driving, participants were asked to rate themselves on driving characteristics that are potentially relevant to anticipatory competence. Significant correlations were found among multiple of these characteristics. Furthermore, all participants also completed the Manchester Driving Behaviour Questionnaire (DBQ), so that correlations between these subjective driving characteristics and DBQ categories could also be investigated. Findings showed that both subjective measures were generally aligned in that the DBQ categories correlated with the subjective characteristics describing them. However, no evidence could be found that participants judging themselves as safe drivers kept longer headways and larger times to collision, and no significant correlations were observed between subjective ratings of fuel-efficiency and fuel-consumption observed in the simulated drive. These findings suggest that caution should be taken when using self-reported driving characteristics to predict actual performance, and future research should further investigate the relation between self-reported measures and simulator performance measures.

Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.

The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis

Objectives Road traffic injuries remain a leading cause of mortality and disability worldwide, especially in low- and middle-income countries. This study aimed to develop and validate a machine-learning model to predict road traffic injury severity, using aberrant driving behaviors as measured by the Manchester Driver Behavior Questionnaire, along with demographic and driving exposure variables. Methods We conducted a secondary analysis of a dataset including 800 drivers. Participants were categorized into three road traffic injury severity classes: no injury (n = 400), mild injury (n = 200), and severe or fatal injury (n = 200). The Boruta feature selection algorithm was applied to confirm predictor relevance. An XGBoost classification model was trained and tuned through 10-fold cross-validation. Model performance was evaluated using multiclass AUC, accuracy, F1 score, and class-specific diagnostic indices. Results The optimized XGBoost model achieved a multiclass AUC of 0.886 and an overall accuracy of 74.4%. Sensitivity was highest for predicting no injury (0.883), while specificity peaked for the severe/fatal injury class (0.910). The most influential predictors included crash history, education level, and daily driving hours. Among behavioral variables, slips and deliberate violations emerged as meaningful contributors to injury severity predictions. Conclusions Integrating psychological constructs from the Driver Behavior Questionnaire with demographic and exposure data provides predictive accuracy in injury severity models. Our behavior-informed framework offers a practical and theoretically grounded tool for identifying high-risk drivers and informing targeted road safety interventions.

This article presents two studies with data from 750 college students (58.67% females, Mage = 20.79years) and 1035 school students (52.1% girls, Mage = 14.44years) respectively, describing the development and initial validation of the Positive and Negative Co-Rumination Scale (PANCRS). The PANCRS consists of 32 items with 3 second-order factors: Positive Co-Rumination consisting of 3 first-order factors (i.e., Affirmation, Problem-Solving and Enhancing Friendship), Negative Co-Rumination consisting of 4 first-order factors (i.e., Worry About Evaluation, Inhibiting Happiness, Worry About Impact and Slack) and Frequency consisting of 2 first-order factors (i.e., Frequencies of Co-Rumination on Positive and Negative Events). Results from exploratory and confirmatory factor analyses confirmed the measure's 9 first-order and 3 second-order factors structure. Moreover, correlation analyses provided first evidence for the subscales' differential validity: (1) Positive Co-Rumination showed positive correlations with positive indicators of psychological adjustment (i.e., friendship quality and life satisfaction) and negative correlations with negative indicators of psychological adjustment (i.e., anxiety and depression); (2) Negative Co-Rumination showed non-significant or negative correlations with positive indicators of psychological adjustment and positive correlations with negative indicators of psychological adjustment; (3) Frequency showed positive correlations with both positive and negative indicators of psychological adjustment. In addition, all PANCRS scores showed satisfactory composite reliability (omegas) and temporal stability (test-retest). Overall the findings suggest that the PANCRS is a reliable and valid instrument to assess positive and negative aspects of Co-rumination.

This study reports on the utilisation of the Manchester Driver Behaviour Questionnaire (DBQ) to examine the self-reported driving behaviours of a large sample of Australian fleet drivers (N = 3414). Surveys were completed by employees before they commenced a one day safety workshop intervention. Factor analysis techniques identified a three factor solution similar to previous research, which was comprised of: (a) errors, (b) highway-code violations and (c) aggressive driving violations. Two items traditionally related with highway-code violations were found to be associated with aggressive driving behaviours among the current sample. Multivariate analyses revealed that exposure to the road, errors and self-reported offences predicted crashes at work in the last 12 months, while gender, highway violations and crashes predicted offences incurred while at work. Importantly, those who received more fines at work were at an increased risk of crashing the work vehicle. However, overall, the DBQ demonstrated limited efficacy at predicting these two outcomes. This paper outlines the major findings of the study in regards to identifying and predicting aberrant driving behaviours and also highlights implications regarding the future utilisation of the DBQ within fleet settings.

The current research aims to test the new measurement model of COVID-19 stressors which includes four stressor types: infection fears, lockdown, economic, and grief in one second-order factor. Using a sample of 2732 from 11 Arab countries and measures for COVID-19 stressors, PTSD, depression, anxiety, and executive function, we conducted exploratory and confirmatory factor analyses, multigroup invariance, and correlations. A second-order factor with four first-order factors model fit the data well. All factors were significantly correlated with PTSD, depression, anxiety, working memory, and inhibition deficits. The measure and its four subscales have robust psychometrics and were invariant across genders.

Assessing the relationship between the Driver Behavior Questionnaire and the Driver Skill Inventory: Revealing sub-groups of drivers

Chapter 4 - Self-Report Instruments and Methods