King's College London

Research portal

Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire

Research output: Contribution to journalArticle

Standard

Measuring errors and violations on the road : A bifactor modeling approach to the Driver Behavior Questionnaire. / Rowe, Richard; Roman, Gabriela D.; McKenna, Frank P.; Barker, Edward; Poulter, Damian.

In: Accident Analysis and Prevention, Vol. 74, 01.01.2015, p. 118-125.

Research output: Contribution to journalArticle

Harvard

Rowe, R, Roman, GD, McKenna, FP, Barker, E & Poulter, D 2015, 'Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire', Accident Analysis and Prevention, vol. 74, pp. 118-125. https://doi.org/10.1016/j.aap.2014.10.012

APA

Rowe, R., Roman, G. D., McKenna, F. P., Barker, E., & Poulter, D. (2015). Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire. Accident Analysis and Prevention, 74, 118-125. https://doi.org/10.1016/j.aap.2014.10.012

Vancouver

Rowe R, Roman GD, McKenna FP, Barker E, Poulter D. Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire. Accident Analysis and Prevention. 2015 Jan 1;74:118-125. https://doi.org/10.1016/j.aap.2014.10.012

Author

Rowe, Richard ; Roman, Gabriela D. ; McKenna, Frank P. ; Barker, Edward ; Poulter, Damian. / Measuring errors and violations on the road : A bifactor modeling approach to the Driver Behavior Questionnaire. In: Accident Analysis and Prevention. 2015 ; Vol. 74. pp. 118-125.

Bibtex Download

@article{97536944b01547bd83388bed20124021,
title = "Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire",
abstract = "The Driver Behavior Questionnaire (DBQ) is a self-report measure of driving behavior that has been widely used over more than 20 years. Despite this wealth of evidence a number of questions remain, including understanding the correlation between its violations and errors sub-components, identifying how these components are related to crash involvement, and testing whether a DBQ based on a reduced number of items can be effective. We address these issues using a bifactor modeling approach to data drawn from the UK Cohort II longitudinal study of novice drivers. This dataset provides observations on 12,012 drivers with DBQ data collected at.5, 1, 2 and 3 years after passing their test. A bifactor model, including a general factor onto which all items loaded, and specific factors for ordinary violations, aggressive violations, slips and errors fitted the data better than correlated factors and second-order factor structures. A model based on only 12 items replicated this structure and produced factor scores that were highly correlated with the full model. The ordinary violations and general factor were significant independent predictors of crash involvement at 6 months after starting independent driving. The discussion considers the role of the general and specific factors in crash involvement.",
keywords = "Bifactor, Confirmatory factor analysis, Driver Behavior Questionnaire, Young drivers",
author = "Richard Rowe and Roman, {Gabriela D.} and McKenna, {Frank P.} and Edward Barker and Damian Poulter",
year = "2015",
month = jan,
day = "1",
doi = "10.1016/j.aap.2014.10.012",
language = "English",
volume = "74",
pages = "118--125",
journal = "Accident Analysis and Prevention",
issn = "0001-4575",
publisher = "Elsevier Limited",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Measuring errors and violations on the road

T2 - A bifactor modeling approach to the Driver Behavior Questionnaire

AU - Rowe, Richard

AU - Roman, Gabriela D.

AU - McKenna, Frank P.

AU - Barker, Edward

AU - Poulter, Damian

PY - 2015/1/1

Y1 - 2015/1/1

N2 - The Driver Behavior Questionnaire (DBQ) is a self-report measure of driving behavior that has been widely used over more than 20 years. Despite this wealth of evidence a number of questions remain, including understanding the correlation between its violations and errors sub-components, identifying how these components are related to crash involvement, and testing whether a DBQ based on a reduced number of items can be effective. We address these issues using a bifactor modeling approach to data drawn from the UK Cohort II longitudinal study of novice drivers. This dataset provides observations on 12,012 drivers with DBQ data collected at.5, 1, 2 and 3 years after passing their test. A bifactor model, including a general factor onto which all items loaded, and specific factors for ordinary violations, aggressive violations, slips and errors fitted the data better than correlated factors and second-order factor structures. A model based on only 12 items replicated this structure and produced factor scores that were highly correlated with the full model. The ordinary violations and general factor were significant independent predictors of crash involvement at 6 months after starting independent driving. The discussion considers the role of the general and specific factors in crash involvement.

AB - The Driver Behavior Questionnaire (DBQ) is a self-report measure of driving behavior that has been widely used over more than 20 years. Despite this wealth of evidence a number of questions remain, including understanding the correlation between its violations and errors sub-components, identifying how these components are related to crash involvement, and testing whether a DBQ based on a reduced number of items can be effective. We address these issues using a bifactor modeling approach to data drawn from the UK Cohort II longitudinal study of novice drivers. This dataset provides observations on 12,012 drivers with DBQ data collected at.5, 1, 2 and 3 years after passing their test. A bifactor model, including a general factor onto which all items loaded, and specific factors for ordinary violations, aggressive violations, slips and errors fitted the data better than correlated factors and second-order factor structures. A model based on only 12 items replicated this structure and produced factor scores that were highly correlated with the full model. The ordinary violations and general factor were significant independent predictors of crash involvement at 6 months after starting independent driving. The discussion considers the role of the general and specific factors in crash involvement.

KW - Bifactor

KW - Confirmatory factor analysis

KW - Driver Behavior Questionnaire

KW - Young drivers

UR - http://www.scopus.com/inward/record.url?scp=84908425864&partnerID=8YFLogxK

U2 - 10.1016/j.aap.2014.10.012

DO - 10.1016/j.aap.2014.10.012

M3 - Article

AN - SCOPUS:84908425864

VL - 74

SP - 118

EP - 125

JO - Accident Analysis and Prevention

JF - Accident Analysis and Prevention

SN - 0001-4575

ER -

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454