Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions. (March 2016)

• Record Type: Journal Article
• Authors: Tay, Richard
• Is Part Of: Accident analysis and prevention. Volume 88(2016)
• Page Start: 52
• Page End: 55
• Publication Date: 2016-03
• Subjects: Logistic model -- Skewed logistic model -- Scobit -- Injury severity; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2015.12.009
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: We compared the standard logistic and skewed logistic models. Injury severity models were estimated using collision data from Victoria, Australia. The symmetry assumption was violated in the serious injury model. The skewed logistic model fitted the unbalanced data better. There are significant differences in the coefficients and marginal effects obtained. Abstract: The binary logistic model has been extensively used to analyze traffic collision and injury data where the outcome of interest has two categories. However, the assumption of a symmetric distribution may not be a desirable property in some cases, especially when there is a significant imbalance in the two categories of outcome. This study compares the standard binary logistic model with the skewed logistic model in two cases in which the symmetry assumption is violated in one but not the other case. The differences in the estimates, and thus the marginal effects obtained, are significant when the assumption of symmetry is violated.