Generalized Linear Models: Loglinear Modeling in Managerial Research
介绍了广义线性模型（GLM）在分析离散多元数据中的应用，重点阐述对数线性建模、probit和逻辑回归等技术的关联，并展示层次对数线性模型在管理研究中的实用性。
In the past three decades we have seen the emergence of the generalized linear model (GLM) techniques for analyzing discrete multivariate data when the independent and dependent variables are categorical, ordinal, or mixed. The primary statistical techniques are loglinear modeling, probit, and logistic regression. The purpose of this article is to (a) briefly describe the emergence of these discrete multivariate techniques in the medical and social sciences, (b) disclose their relationship to one another, and (c) demonstrate the utility of hierarchical loglinear modeling in managerial research