Handbook of Advanced Multilevel Analysis

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advanced multilevel statistical modeling
Bayesian hierarchical models
Category=GPS
Category=JHB
Category=JMB
Category=JNM
cross-classified data analysis
Cross-classified Data Structure
curve
Data Set
Dyad Members
dyadic modeling techniques
educational measurement statistics
effect
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Focal Person
Gibbs Sampler
GMM Estimator
growth
Growth Curve Model
Indistinguishable Dyads
intercept
Longitudinal Factor Model
measurement
Mixture Growth Models
MLR Analysis
model
Multilevel Item Response Theory
Multilevel Latent Class Models
Multilevel SEM
Multiple Membership Models
occasions
optimal experimental design
Posterior Distribution
random
Random Intercept Model
Random Intercepts
Random Regression Coefficients Model
Random Slope
SAS Syntax
SEM Package
Simple Slope
slopes
SPSS Syntax
structural
survival data estimation
Unknown Random Effects

Product details

  • ISBN 9781841697222
  • Weight: 896g
  • Dimensions: 178 x 254mm
  • Publication Date: 19 Jul 2010
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion.

Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis.

Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.

Joop J. Hox is Professor and Chair of Social Science Methodology at Utrecht University. A Fellow of the Royal Statistical Society and a founding member of the European Association of Methodology, his recent publications focus on survey non-response, interviewer effects, survey data quality, missing data, and multilevel analysis of regression and structural equation models.  He is the author of Multilevel Analysis, Second Edition (Routledge) and a co-editor of the International Handbook of Survey Methodology (Routledge). J. Kyle Roberts is an Associate Professor in the Annette Caldwell Simmons School of Education and Human Development at Southern Methodist University. Dr. Roberts has conducted numerous training sessions on multilevel analysis at annual meetings of the American Psychological Association, the American Educational Research Association, and the Southwest Educational Research Association. He has authored several book chapters and articles on multilevel analysis, and currently works with school districts in the development of value-added models for student and teacher