La Tour du Valat

Institut de recherche pour la conservation des zones humides méditerranéennes

Titre :	Data analysis using regression and multilevel/hierarchical models
Auteurs :	Andrew Gelman, Auteur ; Jennifer Hill, Auteur
Type de document :	Ouvrage
Editeur :	Cambridge : Cambridge Univ. Press, 2007
ISBN/ISSN/EAN :	978-0-521-68689-1
Langues:	Anglais
Index. décimale :	MA1 (Mathématiques, Statistiques, Informatique)
Mots-clés :	Modèle ; Analyse données
Résumé :	Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces and demonstrates a wide variety of models, at the same time instructing the reader in how to ﬁt these models using freely available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen in the authors’ own applied research, with programming code provided for each one. Topics covered include causal inference, including regression, poststratiﬁcation, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, ﬁtting, and understanding are provided throughout.

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