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" Fixed effects regression methods for longitudinal data : "
Paul D. Allison.
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BL
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| Record Number
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981979
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b736349
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| Main Entry
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Allison, Paul David.
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| Title & Author
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Fixed effects regression methods for longitudinal data : : using SAS /\ Paul D. Allison.
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| Publication Statement
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Cary, NC :: SAS Institute,, 2005.
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| Page. NO
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1 online resource
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| ISBN
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1590475682
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: 1590477782
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: 1629590274
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: 9781590475683
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: 9781590477786
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: 9781629590271
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1612909604
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| Bibliographies/Indexes
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Includes bibliographical references (pages 139-141) and index.
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| Abstract
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Fixed Effects Regression Methods for Longitudinal Data Using SAS, written by Paul Allison, is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. 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. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. 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 regression models for count data, and PROC CALIS for estimating fixed effects structural equation models. 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. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.
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| Subject
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Linear models (Statistics)
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Longitudinal method.
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Regression analysis.
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Variables (Mathematics)
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Linear models (Statistics)
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Linear models (Statistics)
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Longitudinal method.
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Longitudinal method.
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Mathematical Statistics.
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MATHEMATICS-- Probability Statistics-- Regression Analysis.
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Mathematics.
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Physical Sciences Mathematics.
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Regression analysis.
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Regression analysis.
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Variables (Mathematics)
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Variables (Mathematics)
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SAS (Computer file)
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SAS (Computer file)
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SAS (Computer file)
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| Dewey Classification
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519.536
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| LC Classification
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QA278.2A45 2005eb
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