The Interpretation of Coefficients in N‐Chotomous Qualitative Response Models*
讨论了N分定性响应模型（如有序Probit、多项Logit）中系数的几种解释方法，包括偏导数、概率弹性、敏感度分析和优势比，并用实例说明这些方法能提供不同或更强的结论，帮助研究者更好地理解模型。
Abstract Researchers in financial accounting often use qualitative response models in choice‐based empirical research. Most of this research relies on the familiar techniques of dichotomous probit or logistic regression. Only a limited amount of this research uses n‐chotomous qualitative response models such as ordered probit or multinomial logistic regression. A potential explanation for this limited use is that the interpretation of model coefficients in qualitative response models with limited dependent variables (dichotomous or n‐chotomous) differs substantially from OLS regression, and econometric texts do not provide a systematic approach to coefficient interpretation. This paper discusses several approaches to interpreting coefficients in n‐chotomous qualitative response models. These methods focus on partial derivatives, elasticities of probability, sensitivity analysis, and odds ratios. The methods are applied to the models presented in Thomas (1989) and Mittelstaedt (1989). Additional analyses of the models demonstrate that the methods of interpretation can provide different conclusions or strengthen existing conclusions. The methods provide a better understanding of the directional effects of model coefficients, the relative responsiveness of the probability of choice to changes in the independent variables, and the effects of changes in the independent variables on the probability of choice. These methods should make these models more attractive to researchers interested in choice‐based financial accounting research, and allow for a broader range of decision outcomes than that provided by dichotomous qualitative response models.