Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Fixed-Effects Models
- 3 Models with Random Effects
- 4 Prediction and Bayesian Inference
- 5 Multilevel Models
- 6 Stochastic Regressors
- 7 Modeling Issues
- 8 Dynamic Models
- 9 Binary Dependent Variables
- 10 Generalized Linear Models
- 11 Categorical Dependent Variables and Survival Models
- Appendix A Elements of Matrix Algebra
- Appendix B Normal Distribution
- Appendix C Likelihood-Based Inference
- Appendix D State Space Model and the Kalman Filter
- Appendix E Symbols and Notation
- Appendix F Selected Longitudinal and Panel Data Sets
- References
- Index
Preface
Published online by Cambridge University Press: 05 September 2012
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Fixed-Effects Models
- 3 Models with Random Effects
- 4 Prediction and Bayesian Inference
- 5 Multilevel Models
- 6 Stochastic Regressors
- 7 Modeling Issues
- 8 Dynamic Models
- 9 Binary Dependent Variables
- 10 Generalized Linear Models
- 11 Categorical Dependent Variables and Survival Models
- Appendix A Elements of Matrix Algebra
- Appendix B Normal Distribution
- Appendix C Likelihood-Based Inference
- Appendix D State Space Model and the Kalman Filter
- Appendix E Symbols and Notation
- Appendix F Selected Longitudinal and Panel Data Sets
- References
- Index
Summary
Intended Audience and Level
This text focuses on models and data that arise from repeated measurements taken from a cross section of subjects. These models and data have found substantive applications in many disciplines within the biological and social sciences. The breadth and scope of applications appears to be increasing over time. However, this widespread interest has spawned a hodgepodge of terms; many different terms are used to describe the same concept. To illustrate, even the subject title takes on different meanings in different literatures; sometimes this topic is referred to as “longitudinal data” and sometimes as “panel data.” To welcome readers from a variety of disciplines, the cumbersome yet more inclusive descriptor “longitudinal and panel data” is used.
This text is primarily oriented to applications in the social sciences. Thus, the data sets considered here come from different areas of social science including business, economics, education, and sociology. The methods introduced in the text are oriented toward handling observational data, in contrast to data arising from experimental situations, which are the norm in the biological sciences.
Even with this social science orientation, one of my goals in writing this text is to introduce methodology that has been developed in the statistical and biological sciences, as well as the social sciences. That is, important methodological contributions have been made in each of these areas; my goal is to synthesize the results that are important for analyzing social science data, regardless of their origins.
- Type
- Chapter
- Information
- Longitudinal and Panel DataAnalysis and Applications in the Social Sciences, pp. ix - xviPublisher: Cambridge University PressPrint publication year: 2004