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Multilevel modelling / [edited by] Anders Skrondal and Sophia Rabe-Hesketh.

Contributor(s): Skrondal, Anders [editor] | Rabe-Hesketh, Sophia [editor].
Material type: materialTypeLabelBookSeries: Sage benchmarks in social research methods.Publisher: Los Angeles : SAGE, 2010Description: 4 volumes : illustrations ; 24 cm.ISBN: 9781847872739 (hbk. : 4 vol. set).Subject(s): Social sciences -- Statistical methods | Multilevel models (Statistics)DDC classification: 000SB:300
Contents:
v. 1. Linear multilevel models : model formulation and interpretation -- v. 2. Linear multilevel models : inference, diagnostics and design -- v. 3. Multilevel generalized linear models -- v. 4. Complex models and issues.
Summary: Data collected in the social sciences often have a multilevel or clustered structure. From this we often have research questions that are of a multilevel nature, and multilevel modeling is now widely used across health, economics, demography, education, and many other areas to analyze data clustered within units at higher levels. The editors of this essential four-volume set are among the leading figures of multilevel modeling, an approach which is at once cutting-edge and well established within research methods and the social sciences.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SB:300 Sk629 (Browse shelf) Checked out 23/09/2017 137773
Books Books ISI Library, Kolkata
 
000SB:300 Sk629 (Browse shelf) Checked out 23/09/2017 137774
Books Books ISI Library, Kolkata
 
000SB:300 Sk629 (Browse shelf) Checked out 23/09/2017 137775
Books Books ISI Library, Kolkata
 
000SB:300 Sk629 (Browse shelf) Checked out 23/09/2017 137776
Total holds: 0

Includes bibliographical references.

v. 1. Linear multilevel models : model formulation and interpretation --
v. 2. Linear multilevel models : inference, diagnostics and design --
v. 3. Multilevel generalized linear models --
v. 4. Complex models and issues.

Data collected in the social sciences often have a multilevel or clustered structure. From this we often have research questions that are of a multilevel nature, and multilevel modeling is now widely used across health, economics, demography, education, and many other areas to analyze data clustered within units at higher levels. The editors of this essential four-volume set are among the leading figures of multilevel modeling, an approach which is at once cutting-edge and well established within research methods and the social sciences.

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