Multilevel Analysis: Techniques and Applications, Second Edition

by Hox; Joop J.

• ISBN13: 9781848728462
• ISBN10: 1848728468
• Edition: 2nd
• Format: Nonspecific Binding
• Copyright: 2010-04-26
• Publisher: Routledge

Summary

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 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, epilepsy, 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 multilevel structural models.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 that 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 were retained.A new section on multivariate meta-analysis (Ch. 11).Expanded discussions of covariance structures across time and analyzingããlongitudinal data where noãtrendãis expected.Expanded chapter on Logistic Model for Dichotomous Data and Proportions with new estimation methods.An updated website at www.joophox.net/ 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, ãthe 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.

Table of Contents

Preface	p. viii
Introduction to Multilevel Analysis	p. 1
Aggregation and disaggregation	p. 2
Why do we need special multilevel analysis techniques?	p. 4
Multilevel theories	p. 7
Models described in this book	p. 8
The Basic Two-Level Regression Model	p. 11
Example	p. 11
An extended example	p. 16
Inspecting residuals	p. 23
Three- and more-level regression models	p. 32
A note about notation and software	p. 36
Estimation and Hypothesis Testing in Multilevel Regression	p. 40
Which estimation method?	p. 40
Significance testing and confidence intervals	p. 45
Contrasts and constraints	p. 51
Some Important Methodological and Statistical Issues	p. 54
Analysis strategy	p. 54
Centering and standardizing explanatory variables	p. 59
Interpreting interactions	p. 63
Group mean centering	p. 68
How much variance is explained?	p. 69
Analyzing Longitudinal Data	p. 79
Fixed and varying occasions	p. 80
Example with fixed occasions	p. 81
Example with varying occasions	p. 93
Advantages of multilevel analysis for longitudinal data	p. 98
Complex covariance structures	p. 99
Statistical issues in longitudinal analysis	p. 104
Software issues	p. 111
The Multilevel Generalized Linear Model for Dichotomous Data and Proportions	p. 112
Generalized linear models	p. 112
Multilevel generalized linear models	p. 117
Example: Analyzing dichotomous data	p. 121
Example: Analyzing proportions	p. 123
The ever changing latent scale: Comparing coefficients and variances	p. 133
Interpretation and software issues	p. 139
The Multilevel Generalized Linear Model for Categorical and Count Data	p. 141
Ordered categorical data	p. 141
Count data	p. 151
The ever changing latent scale, again	p. 157
Multilevel Survival Analysis	p. 159
Survival analysis	p. 159
Multilevel survival analysis	p. 163
Multilevel ordinal survival analysis	p. 169
Cross-Classified Multilevel Models	p. 171
Example of cross-classified data: Pupils nested within (primary and secondary schools)	p. 173
Example of cross-classified data: (Sociometric ratings) in small groups	p. 177
Statistical and computational issues	p. 185
Multivariate Multilevel Regression Models	p. 188
The multivariate model	p. 189
Example of multivariate multilevel analysis: Multiple response variables	p. 192
Example of multivariate multilevel analysis: Measuring group characteristics	p. 197
The Multilevel Approach to Meta-Analysis	p. 205
Meta-analysis and multilevel modeling	p. 205
The variance-known model	p. 207
Example and comparison with classical meta-analysis	p. 211
Correcting for artifacts	p. 217
Multivariate meta-analysis	p. 221
Statistical and software issues	p. 228
Appendix	p. 230
Sample Sizes and Power Analysis in Multilevel Regression	p. 233
Sample size and accuracy of estimates	p. 233
Estimating power in multilevel regression designs	p. 237
Advanced Issues in Estimation and Testing	p. 257
The profile likelihood method	p. 259
Robust standard errors	p. 260
Multilevel bootstrapping	p. 264
Bayesian estimation methods	p. 271
Multilevel Factor Models	p. 288
The within and between approach	p. 290
Full maximum likelihood estimation	p. 297
An example of multilevel factor analysis	p. 299
Standardizing estimates in multilevel structural equation modeling	p. 305
Goodness of fit in multilevel structural equation modeling	p. 306
Notation and software	p. 309
Multilevel Path Models	p. 312
Example of a multilevel path analysis	p. 312
Statistical and software issues in multilevel factor and path models	p. 320
Appendix	p. 323
Latent Curve Models	p. 325
Example of latent curve modeling	p. 328
A comparison of multilevel regression analysis and latent curve modeling	p. 335
References	p. 337
Data and Stories	p. 352
Aggregating and Disaggregating	p. 360
Recoding Categorical Data	p. 363
Constructing Orthogonal Polynomials	p. 366
Author Index	p. 369
Subject Index	p. 376
Table of Contents provided by Ingram. All Rights Reserved.