Fixed Effects Regression Methods for Longitudinal Data Using SAS
| Author | : | |
| Rating | : | 4.32 (541 Votes) |
| Asin | : | 1590475682 |
| Format Type | : | paperback |
| Number of Pages | : | 148 Pages |
| Publish Date | : | 2014-04-29 |
| Language | : | English |
DESCRIPTION:
. In 2001 he received the Paul Lazarsfeld Memorial Award for Distinguished Contributions to Sociological Methodology. He has authored several books, including Logistic Regression Using the SAS System: Theory and Application and Survival Analysis Using SAS: A Practical Guide. Paul Allison is Professor of Sociology at the
Gordon S. Linoff said Excellent Book That Does What the Title Says. Recently, I was asked to do some analysis on longitudinal patient data using fixed effects regression. This book provided an excellent, step-by-step approach on how to tackle the problem.The first two chapters cover an overview of fixed effects and random effects modeling in the context of ordinary least squares. Then, Prof. Allison devotes a chapter to each of several topics: modeling binary outcomes, modeling counting outcomes, and modeling time to event (I admit that I stopped at Chapter 5).I am particularly i. Idee Fixe Sylvia D. Hobbs Author provides nice explanation of PROC CALIS for linear fixed effects, random effects and reciprocal effects with lagged predictors. That said, why not in the same volume provide additional discussion clarifying distinction between PROC GLM use of least squares for linear trends and use of PROC CALIS for linear and nonlinear trends and tests for significance in PROC CALIS.. "kind of costly but useful book" according to H., YU-TING. useful book to understand how to conduct sas code with fixed effectalso, it helps understand the concept of the effect for beginners
In a brief monograph, Allison is able to present the essentials of fixed effects for each model and the appropriate procedures in SAS that can implement them. It merits observing that even researchers or students not thoroughly versed in the statistical underpinnings or mathematical complexities will be able to analyze and interpret their data using the directions provided. This is a clear, well-organized, and thoughtful guide to fixed effects models. --Frank Pajares, Emory University . Empirical examples and SAS code are included, making it easier for the reader to
First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. An understanding of logistic regression and Poisson regression is a plus. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regress