Fixed Effects Regression Models

Paul D. Allison

ISBN 10: 0761924973 / ISBN 13: 9780761924975

Editore: Sage Publications, Inc, 2009

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nach der Bestellung gedruckt Neuware - Printed after ordering - This book demonstrates 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.

Codice articolo 9780761924975

Dati bibliografici

• Titolo: Fixed Effects Regression Models
• Editore: Sage Publications, Inc
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 0761924973
• ISBN 13: 9780761924975
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 216 L x 140 A x 8 P (millimetri) 216 millimetri di larghezza per 140 millimetri di altezza per 8 millimetri di profondità
• Peso: 182 grammi

Descrizione articolo

Riassunto

This book demonstrates 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 is 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.

Informazioni sull?autore

Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics.

After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.

Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data.

A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”