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In this important <EM>Handbook</EM>, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The <EM>Handbook</EM> 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.

The SAGE Handbook of Multilevel Modeling

POST SECONDARY FOUR YEAR

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THE SAGE HANDBOOK OF MULTILEVEL MODELING

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
Notes on Contributors.
Preface.
Multilevel Modeling.
1: Multilevel Model Specification and Inference.
2: The Multilevel Model Framework.
3: Multilevel Model Notation&#x2014;Establishing the Commonalities.
4: Likelihood Estimation in Multilevel Models.
5: Bayesian Multilevel Models.
6: The Choice between Fixed and Random Effects.
7: Centering Predictors and Contextual Effects.
8: Model Selection for Multilevel Models.
9: Generalized Linear Mixed Models&#x2013;Overview.
10: Longitudinal Data Modeling.
11: Complexities in Error Structures Within Individuals.
12: Design Considerations in Multilevel Studies.
13: Multilevel Models and Causal Inference.
14: Variations and Extensions of the Multilevel Model.
15: Multilevel Functional Data Analysis.
16: Nonlinear Models.
17: Generalized Linear Mixed Models: Estimation and Inference.
18: Categorical Response Data.
19: Smoothing and Semiparametric Models.
20: Penalized Splines and Multilevel Models.
21: Hierarchical Dynamic Models.
22: Mixture and Latent Class Models in Longitudinal and Other Settings.
23: Multivariate Response Data.
24: Practical Considerations in Model Fit and Specification.
25: Robust Methods for Multilevel Analysis.
26: Missing Data.
27: Lack of Fit, Graphics, and Multilevel Model Diagnostics.
28: Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?.
29: Software for Fitting Multilevel Models.
30: Selected Applications.
31: Meta-Analysis.
32: Modeling Policy Adoption and Impact with Multilevel Methods.
33: Multilevel Models in the Social and Behavioral Sciences.
34: Survival Analysis and the Frailty Model.
35: Point-Referenced Spatial Modeling.
36: Market Research and Preference Data.
37: Multilevel Modeling of Social Network and Relational Data.
Name Index.
Subject Index.

The SAGE Handbook of Multilevel Modeling.css-1p8rvzy{position:absolute;padding-left:8px;}

The SAGE Handbook of Multilevel Modeling