Includes bibliographical references (p. 113-115) and indexes.
About the Author Series Editor's Introduction 1. Introduction 2. Linear 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 Appendix 2 References Author Index Subject Index.
(source: Nielsen Book Data)
This book will show how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book will be appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. (source: Nielsen Book Data)