01664156

Component

Older adults that report self-acknowledged driving difficulty tend to practice driving avoidance behaviors (Owsley, Stalvey, Wells, & Sloane, 1999). Driving avoidance can be assessed using the Driving Habits Questionnaire (Owsley, Wells, & Sloane, 1999). Previous factor analysis of the Driving Habits Questionnaire (DHQ) has revealed a one factor solution for a sample of older adults (Wong, Smith, & Sullivan, 2015). Thus far, a factor analysis of the DHQ has not been done in a sample of younger drivers. The most successful approaches to reducing risky driving behaviors and outcomes for recently licensed teenagers include restricting their exposure to high-risk environments through Graduated Driver Licensing (GDL) provisions and parents’ limit-setting (e.g., behavioral control) (Simons-Morton, Ouimet, & Catalano, 2008; Williams, Tefft, & Grabowski, 2012). However, new drivers need exposure to these environments in order to build their ability to drive safely. Driving calibration have been put forward as a way to describe the psychological processes associated with recognizing the task demands of the driving environment and properly matching them to the emerging skill set of the new driver. By investigating factors that may influence avoidance behaviors in young drivers, researchers can then look further into the role driving avoidance plays in calibration and what feedback is necessary for young drivers to properly assess their own driving abilities. This study was undertaken to determine the factor structure of the DHQ in drivers of different ages. Participants included 378 drivers split into 3 groups; young drivers (less than 21 years of age; n = 131), adult drivers (21-64 years of age; n = 100), and older drivers (age 65 years and up; n = 147). Driving avoidance was measured using 9 items from the DHQ. The 9 items from the DHQ were analyzed for the three age groups using principle axis factoring in SPSS 24. All three age groups were suitable for factor analysis with the Kaiser-Meyer-Olkin values being greater than or equal to.80 and the Bartlett’s Test of Sphericity being statistically significant for all groups. The authors found a two-factor solution for each age group. For the young drivers group, the two factors (general avoidance and traffic congestion/volume avoidance) had eigenvalues of 3.54 and 1.23 and explained 39.3% and 13.7% percent of the variance, respectively. The two factors for the adult drivers group had eigenvalues of 4.88 and 1.04 and explained 54.2% and 11.5% of the variance, respectively. The factor loadings for the adult drivers did not fit into interpretable categories. The older group had two factors (conditions-based avoidance and social/vision-based avoidance) with eigenvalues of 4.27 and 1.05 and explained 47.4% and 11.6% of the variance, respectively. Results indicate a two-factor solution for each age group, “general avoidance” and “traffic congestion/ volume avoidance” for young drivers and “conditions based avoidance and social/vision based avoidance” for the older drivers group. Additional research could determine what motivates younger drivers to avoid certain situations and if avoidance behaviors are protective against motor vehicle crash risk, as they are in older adult populations.

This paper was sponsored by TRB committee ANB30 Standing Committee on Operator Education and Regulation.

18-05333

English

4p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References (5)

Age groups; Aged drivers; Behavior; Drivers; Driving; Teenage drivers; Traffic congestion; Traffic volume

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2018 Paper #18-05333

TRIS, TRB, ATRI

Jan 8 2018 11:21AM