Motorcycle riders would be more vulnerable in the event of a crash because of their lack of protection which would often result in them suffering more severe injuries than car drivers. This paper estimated three crash severity models to identify factors that contributed to increasing the severity of motorcycle involved crashes in the Canadian City of Calgary. We found that results from the ordered logit model, heterogeneous choice model and partially constrained generalized ordered logit model produced estimates that were very similar which attested to their robustness. Injury severity tended to increase in neighborhoods with loops and lollipops types of streets or involved right-angle and left-turn-across-path crashes, a truck, unsafe speed or alcohol use but tended to decrease if the crash occurred in parking lots or during winter, involved a van or male rider, or a rider following-too-closely to the vehicle in front.