Name: Multilevel Models: Applications Using Sas(r) (en Inglés)
Price: 136250.00 CLP
Availability: InStock
Author: Wang, Jichuan ; Xie, Haiyi ; Fisher, James F.
ISBN: 9783110267594

portada Multilevel Models: Applications Using Sas(r) (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

274

Encuadernación

Tapa Dura

Dimensiones

24.4 x 17.5 x 2.0 cm

Peso

0.59 kg.

ISBN

3110267594

ISBN13

9783110267594

Categorías

Probabilidad y estadística
Matemáticas aplicadas
Geofísica

Multilevel Models: Applications Using Sas(r) (en Inglés)

James F. Fisher (Autor) · Jichuan Wang (Autor) · Haiyi Xie (Autor) · de Gruyter · Tapa Dura

Libro Físico

$ 136.250

$ 272.500

Ahorras: $ 136.250

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Origen: España (Costos de importación incluídos en el precio)

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Reseña del libro "Multilevel Models: Applications Using Sas(r) (en Inglés)"

Interest in multilevel statistical models for social science and public health studies has been aroused dramatically since the mid-1980s. New multilevel modeling techniques are giving researchers tools for analyzing data that have a hierarchical or clustered structure. Multilevel models are now applied to a wide range of studies in sociology, population studies, education studies, psychology, economics, epidemiology, and public health. This book covers a broad range of topics about multilevel modeling. The goal of the authors is to help students and researchers who are interested in analysis of multilevel data to understand the basic concepts, theoretical frameworks and application methods of multilevel modeling. The book is written in non-mathematical terms, focusing on the methods and application of various multilevel models, using the internationally widely used statistical software, the Statistics Analysis System (SAS(R)). Examples are drawn from analysis of real-world research data. The authors focus on twolevel models in this book because it is most frequently encountered situation in real research. These models can be readily expanded to models with three or more levels when applicable. A wide range of linear and non-linear multilevel models are introduced and demonstrated.