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Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition

Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpretating models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata. The authors also provide a suite of commands for hypothesis testing and model diagnostics to accompany the book. The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. Binary, ordinal, nominal, and count outcomes are covered in detail in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book. This book is filled with concrete examples. Because all the examples, datasets, and author-written commands are available from the authors’ web site, readers can easily replicate the examples using Stata. This book is ideal for students or applied researchers who want to know how to fit and interpret models for categorical data. For further details or to order online, please visit the Stata Bookstore.

[此贴子已经被作者于2006-5-4 13:01:20编辑过]

Data Analysis Using Stata

Data Analysis Using Stata provides a comprehensive introduction to Stata that will be useful to those who are just learning statistics and Stata as well as users of other statistical packages making the switch to Stata. Throughout the book, the authors make extensive use of examples using data from the German Socioeconomic Panel, a large survey of households containing demographic, income, employment, and other key information.

The book begins with an introduction to the Stata interface and then proceeds with a discussion of Stata syntax and simple programming tools like foreach loops. The core of the book includes chapters on producing tables and graphs, performing linear regression, and using logistic regression. All key concepts are illustrated with multiple examples.

The remainder of the book includes chapters on reading text files, writing programs and ado-files, and Internet resources such as the search command and the SSC archive.

Overall, Kohler and Kreuter's book will serve as a valuable introduction to Stata, both for those who are new to statistics and statistical computing and for those new to Stata but familiar with other programs. The book also makes a handy reference guide for existing Stata users.

For further details or to order online, please visit the Stata Bookstore.

[此贴子已经被作者于2006-5-4 7:46:44编辑过]

Beginning with the comparatively simple random-intercept linear model without covariates, the text develops the mixed model from first principles, familiarizing the reader with terminology, summarizing and relating the widely used estimating strategies, and providing historical perspective.

Once this mixed-model foundation has been established, the text smoothly generalizes to random-intercept models with covariates and then to random-coefficient models. The middle chapters of the text apply the concepts defined earlier for Gaussian models to models for binary responses (e.g., logit and probit), ordinal responses (e.g., ordered logit and ordered probit), and count responses (e.g., Poisson). Models with multiple levels of random variation are then considered, as well as models with crossed (nonnested) random effects. The datasets used are real data from the medical, social, and behavioral sciences literature, and several thought-provoking exercises are included at the end of each chapter.

The text is loaded with applications of generalized mixed models performed in Stata. The authors are the developers of gllamm, a Stata program that can fit a vast array of latent-variable models, of which the generalized linear mixed model is a special case. With the release of version 9, Stata introduced the xtmixed command for fitting linear (Gaussian) mixed models. These two commands, combined with the rest of the xt suite of Stata commands (e.g., xtlogit, xtprobit), can be used in conjunction to perform comparative mixed-model analyses for various response families. The types of models fit by these commands sometimes overlap, and when this occurs the authors highlight the differences in syntax, data organization, and output for the two (or more) commands that can be used to fit the same model. The text also points out the relative strengths and weaknesses of each command when used to fit the same model, based on issues such as computational speed, accuracy, and available predictions and postestimation statistics. In particular, the relationship between gllamm and xtmixed and how they complement each other is made very clear.

In summary, this text is the most complete and up-to-date depiction of Stata's capacity for fitting generalized linear mixed models and an ideal introduction for Stata users wishing to learn about this powerful data-analysis tool.

Stata help for xtmixed

[此贴子已经被作者于2006-5-4 7:52:43编辑过]