IDENTIFYING BEST PRACTICES STATES IN MOTORCYCLE RIDER EDUCATION AND LICENSING

Motorcycle rider education and licensing play key roles in reducing motorcycle crashes and injuries, yet little is known about what constitutes effective rider training and licensing. This article reports on a study that was undertaken to develop a model of best practices in motorcycle rider education and licensing. The authors then identify states that most closely adhere to the model. Data used was from the 2001 National Highway Traffic Safety Administration (NHTSA) study that included 47 states that offer state-legislated motorcycle rider education. Results showed that 10 states were classified as high best practice states: Oregon, Delaware, Idaho, Nevada, New Mexico, Maryland, Ohio, Hawaii, Washington, and Minnesota. These are states that satisfy to some extent most of the key best practices identified by the model. Eight states were classified as low best practices scores: Kansas, Arizona, Kentucky, New Jersey, West Virginia, Rhode Island, and South Carolina. These are states that engage in very few of the best practices identified in the model. All other states fall somewhere in the middle, in terms of best practices. Scores for each state are also presented for three subscales: program administration, rider education, and licensing. States with high best practices have lower motorcycle fatality rates than their lower scoring counterparts. The authors conclude that the model is designed to help motorcycle safety administrators, policymakers, and researchers identify the factors that contribute to high quality rider education and licensing.

  • Availability:
  • Corporate Authors:

    Elsevier

    360 Park Avenue South
    New York, NY  United States  10011-1710
  • Authors:
    • Baldi, S
    • Baer, J D
    • Cook, A L
  • Publication Date: 2005

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 00987568
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Mar 30 2005 12:00AM