The comparison of coefficients of logit models obtained for different groups is widely considered as problematic because of possible heterogeneity of residual variances in latent variables. It is shown that the heterogeneous logit model can be used to account for this type of heterogeneity by considering reduced models that are identified. A model selection strategy is proposed that can distinguish between effects that are due to heterogeneity and substantial interaction effects. In contrast to the common understanding, the heterogeneous logit model is considered as a model that contains effect modifying terms, which are not necessarily linked to variances but can also represent other types of heterogeneity in the population. The alternative interpretation of the parameters in the heterogeneous logit model makes it a flexible tool that can account for various sources of heterogeneity. Although the model is typically derived from latent variables it is important that for the interpretation of parameters the reference to latent variables is not needed. Latent variables are considered as a motivation for binary models, but the effects in the models can be interpreted as effects on the binary response.

中文翻译：

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异质性建模：关于通过逻辑回归进行的组比较问题和异质选择模型的潜力

由于潜在变量中残差方差可能存在异质性，因此针对不同组获得的logit模型系数进行比较被普遍认为是有问题的。结果表明，通过考虑已确定的简化模型，异构logit模型可用于解决此类异构问题。提出了一种模型选择策略，可以区分由于异质性造成的影响和实质性的交互作用。与通常的理解相反，异类logit模型被认为是包含效应修正项的模型，这些效应修正项不一定与方差相关，但也可以表示总体中的其他类型的异质性。异构logit模型中参数的替代解释使其成为一种灵活的工具，可以解释各种异构源。尽管模型通常是从​​潜在变量中得出的，但重要的是，对于参数的解释，不需要引用潜在变量。潜在变量被认为是二元模型的动机，但是模型中的影响可以解释为对二元响应的影响。