Regression Models for Categorical and Limited Dependent Variables
- J. Scott Long - Indiana University, USA
After a review of the linear regression model and an introduction to maximum likelihood estimation, the book then: covers the logit and probit models for binary outcomes; reviews standard statistical tests associated with maximum likelihood estimation; and considers a variety of measures for assessing the fit of a model. J Scott Long also: extends the binary logit and probit models to ordered outcomes; presents the multinomial and conditioned logit models for nominal outcomes; considers models with censored and truncated dependent variables with a focus on the tobit model; describes models for sample selection bias; presents models for count outcomes by beginning with the Poisson regression model; and compares the models from earlier chapters, discussing the links between these models and others not discussed in the book.
"Regression Models for Categorical and Limited Dependent Variables excels at explaining applications of nonlinear regression models. . . The book provides much practical guidance for the estimation, identification, and validation of models for CLDVs. Each chapter is interspersed with exercises and helpful questions. In summary, the author exceeds his goal to provide ‘a firm foundation’ for further reading from the vast and growing literature on limited and categorical dependent variables."
Stellar explanation of generalized linear models.
This is a very well-organized, complete treatment of the analysis of limited qualitiative dependent variables. It also has a complimentary text (which unfortunately is not published by Sage) that guides for exercises with Stata.
The book gives a deep insight into the models of binary and categorical variables. The theoretical work is well done and is therefore ideal as an accompanying textbook.
The book is a nice introduction to the subject material and is largely accessible even to students with little college-level mathematics training. The subject matter is presented clearly and in a manner useful to applied researchers.