SAGE handbook of multilevel modeling / ed. by Marc A. Scott. , Jeffrey S. Simonoff and Brian D. Marx

Contributor(s):

Material type: Text

TextLanguage: English Publication details: Los Angeles : SAGE Publishings, 2013.Description: xxxv, 657 p. : illISBN:

9780857025647

Subject(s):

Social sciences \x Research \x Mathematical models

DDC classification:

300.151 SCO/S

Summary: In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode	Item holds
Technical Reference Book	Central Library, IIT Bhubaneswar	Central Library, IIT Bhubaneswar	300.151 SCO/S (Browse shelf(Opens below))	Available		6713

Total holds: 0

Includes Index.

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.

The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference.
Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models.
Part III includes discussion of missing data and robust methods, assessment of fit and software.
Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines.
Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.

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