Multilevel Analysis

  • Multilevel Analysis
  • Multilevel Analysis
  • Multilevel Analysis
  • Multilevel Analysis

Multilevel Analysis

The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis.


Snijders and Bosker's book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis.


This book provides step-by-step coverage of:


* multilevel theories


* ecological fallacies


* the hierarchical linear model


* testing and model specification


* heteroscedasticity


* study designs


* longitudinal data


* multivariate multilevel models


* discrete dependent variables


There are also new chapters on:


* missing data


* multilevel modeling and survey weights


* Bayesian and MCMC estimation and latent-class models.


This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix.


This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis.


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The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis.


Snijders and Bosker's book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis.


This book provides step-by-step coverage of:


* multilevel theories


* ecological fallacies


* the hierarchical linear model


* testing and model specification


* heteroscedasticity


* study designs


* longitudinal data


* multivariate multilevel models


* discrete dependent variables


There are also new chapters on:


* missing data


* multilevel modeling and survey weights


* Bayesian and MCMC estimation and latent-class models.


This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix.


This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis.


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