Summary: Class tested at two major universities and written by an award-winning teacher, J. Scott Long's book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader t ...show moreo see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity.
The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted--and reviews standard statistical tests associated with maximum likelihood estimation and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample selection bias and presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model and zero inflated count models. He concludes by comparing and contrasting the models from earlier chapters and discussing the links between these models and models not discussed in the book, such as loglinear and event history models. Helpful exercises are included in the book with brief answers included in the appendix so that readers can practice the techniques as they read about them. ...show less
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