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Multilevel Analysis Techniques and Applications

ISBN-10: 1848728468
ISBN-13: 9781848728462
Edition: 2nd 2010 (Revised)
Authors: Joop Hox
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Description: This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions making it useful as both an introduction and as a reference to both students,  More...

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Book details

Edition: 2nd
Copyright year: 2010
Publisher: Taylor & Francis Group
Publication date: 5/24/2010
Binding: Paperback
Pages: 392
Size: 6.00" wide x 8.75" long x 0.75" tall
Weight: 1.254
Language: English

This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions making it useful as both an introduction and as a reference to both students, researchers, and methodologists. Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines. For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epillepsy, divorce, and sociometric scores, to name a few. The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files. Readers are introduced to both the multilevel regression model and models for covariance structures.Highlights of the second edition include:Two new chapters—one on Multilevel Models for Ordinal and Count Data (Ch. 7) and another on Multilevel Survival Analysis (Ch. 8).Thoroughly unpdated chapters on multilevel structural equation modeling reflect the enormous technical progress of the last few years.The addition of some simpler examples to help the novice, but the more complex examples that combine more than one problem have been retained.A new section on multivariate meta-analysis (Ch. 11).Expanded chapter on Analyzing Longitudinal Data with a better discussion of covariance structures across time and analyzing data where no growth curve is expected.Expanded chapter on Logistic Model for Dichotomous Data and Proportions with new estimation methods.Extensively revised and updated chapter on Multilevel factor models.An Updated website at www.geocities.com/joophox with data sets for all the text examples and up to date screen shots and PowerPoint slides for Instructors.Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including psychology, education, sociology, the health sciences, and business. The advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines. A basic understanding of ANOVA and multiple regression is assumed. The section on multilevel structural equation models assumes a basic understanding of SEM.

Preface
Introduction to Multilevel Analysis
Aggregation and disaggregation
Why do we need special multilevel analysis techniques?
Multilevel theories
Models described in this book
The Basic Two-Level Regression Model
Example
An extended example
Inspecting residuals
Three- and more-level regression models
A note about notation and software
Estimation and Hypothesis Testing in Multilevel Regression
Which estimation method?
Significance testing and confidence intervals
Contrasts and constraints
Some Important Methodological and Statistical Issues
Analysis strategy
Centering and standardizing explanatory variables
Interpreting interactions
Group mean centering
How much variance is explained?
Analyzing Longitudinal Data
Fixed and varying occasions
Example with fixed occasions
Example with varying occasions
Advantages of multilevel analysis for longitudinal data
Complex covariance structures
Statistical issues in longitudinal analysis
Software issues
The Multilevel Generalized Linear Model for Dichotomous Data and Proportions
Generalized linear models
Multilevel generalized linear models
Example: Analyzing dichotomous data
Example: Analyzing proportions
The ever changing latent scale: Comparing coefficients and variances
Interpretation and software issues
The Multilevel Generalized Linear Model for Categorical and Count Data
Ordered categorical data
Count data
The ever changing latent scale, again
Multilevel Survival Analysis
Survival analysis
Multilevel survival analysis
Multilevel ordinal survival analysis
Cross-Classified Multilevel Models
Example of cross-classified data: Pupils nested within (primary and secondary schools)
Example of cross-classified data: (Sociometric ratings) in small groups
Statistical and computational issues
Multivariate Multilevel Regression Models
The multivariate model
Example of multivariate multilevel analysis: Multiple response variables
Example of multivariate multilevel analysis: Measuring group characteristics
The Multilevel Approach to Meta-Analysis
Meta-analysis and multilevel modeling
The variance-known model
Example and comparison with classical meta-analysis
Correcting for artifacts
Multivariate meta-analysis
Statistical and software issues
Appendix
Sample Sizes and Power Analysis in Multilevel Regression
Sample size and accuracy of estimates
Estimating power in multilevel regression designs
Advanced Issues in Estimation and Testing
The profile likelihood method
Robust standard errors
Multilevel bootstrapping
Bayesian estimation methods
Multilevel Factor Models
The within and between approach
Full maximum likelihood estimation
An example of multilevel factor analysis
Standardizing estimates in multilevel structural equation modeling
Goodness of fit in multilevel structural equation modeling
Notation and software
Multilevel Path Models
Example of a multilevel path analysis
Statistical and software issues in multilevel factor and path models
Appendix
Latent Curve Models
Example of latent curve modeling
A comparison of multilevel regression analysis and latent curve modeling
References
Data and Stories
Aggregating and Disaggregating
Recoding Categorical Data
Constructing Orthogonal Polynomials
Author Index
Subject Index

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