Citation

Mattsson M. Accid. Anal. Prev. 2012; 48: 379-396.

Affiliation

Traffic Research Unit, Institute of Behavioural Sciences, University of Helsinki, PO Box 9 (Siltavuorenpenger 1 A), FI-00014 University of Helsinki, Finland.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.aap.2012.02.009

PMID

22664704

Abstract

The Driver Behaviour Questionnaire (DBQ) is perhaps the most widely used questionnaire instrument in traffic psychology with 174 studies published by late 2010. The instrument was developed based on a plausible cognitive ergonomic theory (the Generic Error Modeling System, GEMS), but the factor structure obtained in the original study (Reason et al., 1990) did not mirror the theory's conceptual structure. This led to abandoning GEMS and adopting the obtained factor structure as a starting point for further DBQ research. This article argues that (1) certain choices in the original study, concerning statistical methodology and the wording of individual question items, may have contributed to the ways the obtained factor structure deviated from the underlying theory and (2) the analysis methods often used in DBQ studies, principal components (PC) analysis and maximum likelihood (ML) factor analysis, are not optimal choices for the non-normally distributed categorical data that is obtained using the instrument. This is because ML produces biased results when used with this type of data, while PC is by definition unable to uncover latent factors as it summarizes all variation in the measured variables. (3) Even though DBQ factor scores have been routinely compared in subgroups of men and women and respondents of different ages, DBQ's factorial invariance in these groups has not been rigorously tested. These concerns are addressed in this article by framing the results of certain previous DBQ studies as a structural equation model (SEM) and an Exploratory Structural Equation Model (ESEM) and testing measurement model fit in subgroups of respondents. The SEM analyses indicate that the model does not fit data from the whole sample of respondents as it stands, while the ESEM analyses show that a modification of the model does. However, the ESEM analyses indicate the DBQ measures different underlying latent variables in the different subgroups. Based on the analyses and a review of recent advances in attention and memory research, an update to the theory underlying the DBQ is suggested.

Language: en