Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 3
3 CHAPTER ONE INTRODUCTION BACKGROUND tions) is most indicative, although nondriving behavioral history (e.g., criminal record) is predictive of risk as well Research reveals large and enduring individual differences (Knipling et al. 2004). in crash risk among commercial drivers. These differences pervade the general population of drivers as well. Most com- Past research highlights the need for valid and usable mercial drivers are reliable and safe, but a relatively small driver selection procedures for carriers. These selection percentage (perhaps 10%15%) is heavily overinvolved in procedures seek to measure enduring individual differences crashes and incidents. This phenomenon has been termed relevant to driving safety. Ideally, companies could employ a differential driver risk. CTBSSP Synthesis 4 (Knipling et al. battery of tests, measurements, and questionnaires designed 2004) explored differential driver risk and high-risk drivers to fairly measure these driver traits and thereby identify in particular. Evidence comes from various sources. Most likely high- and low-risk drivers. Research indicates that no compelling are naturalistic driving studies, which use instru- one test is likely to be definitive. Tests must be validated mented vehicles to reliably count driver involvements in at- against job performance criteria such as crash, violation, and fault driving events, including crashes, near-crashes, and other incident rates. The opportunity for improving the quality of incidents. Event counts can be compared with driver exposure fleet drivers is strongest when, owing to economic or other (e.g., driving hours) to generate rates of driver involvements conditions, there are larger numbers of commercial driver in at-fault events. The observed individual differences in applicants and carriers can afford to be more selective in hir- driver risk are far greater than could possibly occur by chance ing. A caveat, however, is that greater selectivity increases variation alone. For example, in one major large truck natural- fleet driver quality only if a fleet employs valid selection istic driving study (Hickman et al. 2005), a subset of drivers procedures and devices. Multiple devices have the greatest with just 19% of driving exposure was involved in 53% of all combined benefit when they tap into different driver traits observed at-fault driving events. The remaining drivers, with and dimensions relating to risk. 81% of exposure, had just 47% of at-fault events. One constraint on the use of selection tests in hiring Although some drivers may change their driving styles commercial drivers is that all employee selection tests must for better or worse over time, most individual differences meet Equal Employment Opportunity Commission (EEOC) in driver risk are persistent (Miller and Schuster 1983; Lan- standards for test validity. These standards help to ensure caster and Ward 2002). Indeed, many individual differ- that tests fairly capture job performance-related personal ences in human performance and behavior are influenced dimensions and do not arbitrarily discriminate based on by heredity (Larson and Buss 2005; Thiffault 2007). Prin- non-performance-related applicant characteristics. There cipal correlates of differential driver risk include personal- are several ways of demonstrating job test validity. This ity dimensions such as sensation-seeking, anger/hostility, report will review these requirements as they relate to car- impulsivity, intensity (i.e., "Type A"), agreeableness, and rier practices to help ensure fair, legal, and safety-effective conscientiousness. Individual perceptions and attitudes driver selection. This information, together with informa- about risk are reflective of personality and of course affect tion on the tests themselves and their use within commercial safety-related behaviors and outcomes. Mental abilities (e.g., transport, can be a useful foundation for carriers to make spatial, mathematical) are also related to commercial driving greater use of selection devices as well as better choices safety and other measures of employee success (Burks et al. among available instruments. Selection tests and measure- 2009). Driver risk can also be related to driver physical and ments for use by carriers will be described within the overall sensorimotor abilities, such as dynamic vision, information framework of commercial driver qualifications, licensing, processing proficiency, and reaction time. Various medical and federal requirements for the fair use of employee selec- conditions are also associated with driver risk, including tion instruments. cardiovascular illness, sleep apnea, other sleep disorders, diabetes, and obesity. Behavioral history ("biodata") is also The safe selection of commercial drivers may be seen predictive of commercial driver risk (Murray et al. 2005). within at least two larger contexts. The first of these is crash Driving behavioral history (i.e., crashes and moving viola- risk factors in general. Much of road safety research seeks to