title: Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions.
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.
authors.affiliation: School of Business IT and Logistics, RMIT University, Melbourne, Victoria, Australia. Electronic address: rtay888@gmail.com.
authors.forename: Richard
authors.initials: R
authors.lastname: Tay
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D012622:Seat Belts; D012737:Sex Factors; D013997:Time Factors; D015599:Trauma Severity Indices; D014739:Victoria / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000401:mortality; D055815:Young Adult
keywords: Injury severity; Logistic model; Scobit; Skewed logistic model