Written at a level appropriate for anyone who has ever taken an introductory statistics course, this compact (5.5x8.5") text looks at the advantages and disadvantages of types of regression methods called fixed effects models, such as those for continuous, categorical, count dependent variables, and those found in structural equation settings. Fixed effects models are compared with random effects models, and the estimation and interpretation of fixed effects models is demonstrated in a variety of different contexts. Examples rely on the Stata package, and the appendix supplies Stata programs for all of the examples in the book. The text is intended as a supplement for graduate-level courses in regression, linear regression, intermediate/advanced statistics, and regression and correlation. It will also aid researchers who are using repeated measures or cross- sectional data. Readers should understand basic principles of statistical inference, and should also be familiar with the regression methods on which the fixed effects methods are based (linear regression, logistic regression, Poisson and negative binomial regression, Cox regression, and linear structural equation modeling). Allison teaches advanced graduate courses in sociology at the University of Pennsylvania. Annotation ⓒ2009 Book News, Inc., Portland, OR (booknews.com)

CONTENTS 1. Introduction 2. Liner Fixed Effects Models: Basics 3. Fixed Effects Logistic Models 4. Fixed Effects Models for Count Data 5. Fixed Effects Models for Events History Data 6. Structural Equation Models With Fixed Effects Appendix 1. Stata Programs for Examples in Chapters 2 to 5 Appendix 2. Mplus Programs for Examples in Chapters 6