Abstract:
Collisions
involving police vehicles or occurring as a result of police activity resulted in
over 2000 casualties and 31 deaths in
England and Wales in 2003-4. The assessment of
police driver risk is currently restricted to subjective evaluations carried out by members of
each force's
police driver training team. Objective risk assessments are becoming
increasingly popular with private companies operating fleets of vehicles, in an attempt to
target training interventions at the drivers who are most at risk of collision involvement.
This thesis
reports the development of a psychometric measure of police driver risk using
a series of
qualitative and quantitative methods. The Driver Stress Inventory (DSI) was
administered to 302
police drivers to establish its suitability as a basis on which to build
the Police Driver Risk Index
(PDRI). The Driver Stress Inventory is a well-established
psychometric measure of driver stress, which consists of five behavioural factors and five
coping factors. The DSI factor structure was largely replicated for the police sample. The
generation of new police-specific items was facilitated by a series of in-depth semi-
structured interviews with standard and advanced
police drivers. The transcripts were
analysed for common themes and items were generated from this analysis. The new
PDRI was
administered to a sample of 333 police drivers, and a Principal Components
Analysis was carried out in order to establish the new factor structure and identify items
contributing to the risk profile. Further refinement was carried out using an Item
Discrimination
Analysis, which allowed the length of the test to be reduced further. The
effects of
demographic and situational data on PDRI factor scores was investigated, and
group differences were reported for age, sex, driving experience, and collision
involvement. Test-retest
reliability was investigated and a validation study using
observational data, driver self-assessment and trainer assessment was carried out. The
Police Driver Risk Index will be used to
identify drivers at «highest risk of involvement in a
collision, and pinpoint the areas in which they require remedial training. This will allow
police forces to target their driver training resources more effectively.