The Manchester driver behaviour questionnaire: self-reports of aberrant behaviour among Czech drivers
DOI: 10.1007/s12544-014-0147-z
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Summary
This study addresses the need to distinguish between different forms of aberrant driver behavior and their psychological origins, specifically within the Czech Republic. While the Manchester Driver Behaviour Questionnaire (DBQ) is widely used globally to assess risky driving, it had not previously been translated or localized for Czech drivers. The research aimed to determine the factors affecting driving behavior, establish a valid factor model for the Czech population, identify the roles of demographic variables (age, gender, mileage, social status), and examine the relationship between self-reported behavior and accident involvement. The researchers translated and adapted the original 50-item DBQ into Czech, ensuring cultural and linguistic appropriateness through multiple translation steps and a pilot survey. The final instrument used a six-point frequency scale. Data were collected via an online survey from 2,684 Czech drivers (1,791 men and 893 women). The sample was predominantly young, with 70% under age 27, and included students and employees. Participants completed the DBQ along with a 22-item questionnaire on driving history and sociodemographic data. The data were analyzed using principal components analysis with varimax rotation to identify behavioral factors, and multiple regression analyses to determine predictors for these factors. The analysis confirmed a three-factor structure explaining 31.75% of the total variance. Factor 1, "Dangerous Violations" (18.07% variance), included intentional rule-breaking such as racing and aggressive overtaking. Factor 2, "Dangerous Errors" (10.18% variance), comprised unintentional mistakes like misjudging gaps or failing to check mirrors. Factor 3, "Not Paying Attention to Driving, Straying, and Loss of Orientation" (3.51% variance), was identified as a new factor distinct from previous DBQ adaptations, involving issues like missing exits and getting lost. Regression analyses revealed that Dangerous Violations were predicted by younger age, male gender, higher annual mileage, lower education, urban residence, and freelance occupation. Dangerous Errors were predicted by female gender, residence in smaller towns, and lower lifetime mileage. The new Factor 3 was associated with older age, female gender, higher lifetime mileage, and urban residence. The findings validate the DBQ’s utility in the Czech context while highlighting a unique third factor related to orientation and attention failures. The study demonstrates that demographic and experiential factors significantly predict specific types of aberrant behavior. For instance, violations are linked to younger, experienced urban males, whereas errors are more common among less experienced drivers from rural areas. The identification of the "Loss of Orientation" factor suggests that attention and memory failures constitute a distinct category of risk. These results have practical implications for driver education and training, suggesting that preventive measures should be tailored to specific demographic groups and behavioral profiles rather than treating aberrant behavior as a monolithic construct.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
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- dbq psychometrics
- human error taxonomy
- sex gender
- cultural cross national
- pre crash contributing factors
- personality driving
Information type
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- Empirical Findings: observational prevalence
- Theoretical Contribution: theory or model, computational model