Isbn: 9780521867061 - data analysis using regression and multilevel/hierarchical models (analytical methods for social research) (8 Ergebnisse)

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Hardback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

hardcover. Zustand: Very Good.

Zustand: New. In English.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 648 | Sprache: Englisch | Produktart: Bücher | This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Zustand: New. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Series Editor(s): Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. Series: Analytical Methods for Social Research. Num Pages: 648 pages, 160 exercises. BIC Classification: JHBC; PBK. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 181 x 40. Weight in Grams: 1280. . 2006. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 1st edition. 625 pages. 10.25x7.25x1.50 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, 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 a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes 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.…