In order to identify causational information for accidents, propose preventive countermeasures, and reduce the number of accident casualties, statistical models and econometric models are prevalently employed to analyze the historical accident data. However, due to certain factors including the complexity of accident data and the limitations of models, many problems remain to be explored in accident analysis. Based on previous research findings, this study conducted a crash severity analysis by the following steps: the classification of road accident severity, identification of miscellaneous factors (e.g., driver, vehicle, roads, environment, and management), introduction of covariance theory to analyze the interaction effects among factors. Methodologically, this study used the heteroskedasticity ordinal logit (HORL) model to investigate road traffic accident data, which also utilized T test, information criterion test, likelihood ratio test, chi-square test, and pseudo chi-square test to test parameter estimation and model fit. The accident data were sampled from 5,023 crash records in the HSIS (Highway Safety Information System) database housed in North Carolina, which verified the statistical methodology employed herein. The research found road accident data has heteroskedasticity, and orthogonalization processing and independent distribution processing can avoid heteroskedasticity. The fixed-variance logit model has better applicability to this. HORL is specific to the treatment of accident data with variable variance, which can effectively capture factor heterogeneity and tap into more potential latent variables.