Multilevel and Longitudinal Modeling with IBM SPSS
To Be Published July 23rd 2013 by Routledge – 480 pages
Series: Quantitative Methodology Series
This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110.
Highlights of the new edition include:
The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses.
Ideal as a supplementary text for graduate courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology, this book’s practical approach also appeals to researchers in these fields. The book provides an excellent supplement to Heck & Thomas’s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.
Reviews of the first edition
"With its thorough coverage of the statistical underpinnings of multilevel modeling and the detailed step-by-step instructions on how to analyze data with IBM SPSS, this text is a gold mine for graduate instruction!" -Laura M. Stapleton, University of Maryland, Baltimore County, USA
"This text has both depth and breadth of coverage with material that is accessible and transparent to the novice but at the same time comprehensive for the experienced researcher. It is one of those rare texts that is thorough in both the how-tos of the software and the concepts. It is a key multilevel text that any multilevel researcher will not want to be without." - Debbie L. Hahs-Vaughn, University of Central Florida, USA
"This book is a timely and valuable addition. Multilevel modeling is now becoming much more accessible to practitioners, many of whom use SPSS for other analyses. Therefore, a book like this [is] a great resource …I would purchase the book and require it for my courses.… It is a unique contribution to the field…I wish I had thought of writing it first!" - Dick Carpenter, University of Colorado, Colorado Springs, USA
"The first on the market to explain and illustrate how these multilevel models can be analyzed using the popular SPSS program." - George Marcoulides, University of California, Riverside, USA
1. Introduction to Multilevel Modeling with IBM SPSS. 2. Preparing and Examining the Data for Multilevel Analyses.
3. Defining a Basic Two-Level Multilevel Regression Model. 4. Three-Level Univariate Regression Models. 5. Examining Individual Change with Repeated Measures Data. 6. Applications of Mixed Models for Longitudinal Data.
7. Multivariate Multilevel Models. 8. Cross-Classified Multilevel Models. 9. Concluding Thoughts. Appendix A: Syntax Statements. Appendix B: Model Comparisons Across Software Applications. Appendix C: Syntax Routine to Estimate Rho from Model’s Variance Components.