Multilevel Modeling

G. David Garson is a full professor of public administration at North Carolina State University, where he teaches courses on advanced research methodology, geographic information systems, information technology, e-government, and American government. In 1995 he was recipient of the Donald Campbell Award from the Policy Studies Organization, American Political Science Association, for outstanding contributions to policy research methodology and in 1997 of the Aaron Wildavsky Book Award from the same organization. In 1999 he won the Okidata Instructional Web Award from the Computers and Multimedia Section of the American Political Science Association, in 2002 received an NCSU Award for Innovative Excellence in Teaching and Learning with Technology, and in 2003 received an award "For Outstanding Teaching in Political Science" from the American Political Science Association and the National Political Science Honor Society, Pi Sigma Alpha. In 2008 the NCSU Public Administration Program was named in the top 10 PA schools in the nation in information systems management. Prof. Garson is editor of and contributor to Handbook of Public Information Systems, Third Edition.(2010); Handbook of Research on Public Information Technology (2008), Patriotic Information Systems: Privacy, Access, and Security Issues of Bush Information Policy (2008), Modern Public Information Technology Systems (2007), and author of Public Information Technology and E-Governance: Managing the Virtual State (2006), editor of Public Information Systems: Policy and Management Issues (2003), coeditor of Digital Government: Principles and Practices (2003), coauthor of Crime Mapping (2003), author of Guide to Writing Quantitative Papers, Theses, and Dissertations (Dekker, 2001), editor of Social Dimensions of Information Technology (2000), Information Technology and Computer Applications in Public Administration: Issues and Trends (1999) and is author of Neural Network Analysis for Social Scientists (1998), Computer Technology and Social Issues (1995), Geographic Databases and Analytic Mapping (1992), and is author, coauthor, editor, or coeditor of 17 other books and author or coauthor of over 50 articles. He has also created award-winning American Government computer simulations, CD-ROMs, and six web sites for Prentice-Hall and Simon & Schuster (1995-1999). For the last 28 years he has also served as editor of the Social Science Computer Review and is on the editorial board of four additional journals. His widely-cited online textbook, Statnotes: Topics in Multivariate Analysis (2006-2009), is used by over 1.5 million people a year. Professor Garson received his undergraduate degree in political science from Princeton University (1965) and his doctoral degree in government from Harvard University (1969).

Preface Acknowledgments About the Author Chapter 1 * Introduction to Multilevel Modeling Overview What Multilevel Modeling Does The Importance of Multilevel Theory Types of Multilevel Data Common Types of Multilevel Model Mediation and Moderation Models in Multilevel Analysis Alternative Statistical Packages Multilevel Modeling Versus GEE Summary Glossary Challenge Questions With Answers Chapter 2 * Assumptions of Multilevel Modeling About This Chapter Overview Model Specification Construct Operationalization and Validation Random Sampling Sample Size Balanced and Unbalanced Designs Data Level Linearity and Nonlinearity Independence Recursivity Missing Data Outliers Centered and Standardized Data Longitudinal Time Values Multicollinearity Homogeneity of Error Variance Normally Distributed Residuals Normal Distribution of Variables Normal Distribution of Random Effects Convergence Covariance Structure Assumptions Summary Glossary Challenge Questions With Answers Chapter 3 * The Null Model Overview Testing the Need for Multilevel Modeling Likelihood Ratio Tests Partition of Variance Components Examples Summary Glossary Challenge Questions With Answers Chapter 4 * Estimating Multilevel Models Fixed and Random Effects Why Not Just Use OLS Regression? Why Not Just Use GLM (ANOVA)? Types of Estimation Robust and Cluster-Robust Standard Errors Summary Glossary Challenge Questions With Answers Chapter 5 * Goodness of Fit and Effect Size in Multilevel Models Overview Goodness of Fit Measures and Tests Effect Size Measures Effect Size and Endogeneity Summary Glossary Challenge Questions With Answers Chapter 6 * The Two-Level Random Intercept Model Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 7 * The Two-Level Random Coefficients Model Overview SPSS Stata SAS HLM 7 R Significance (p) Values for Variance Components Summary Glossary Challenge Questions With Answers Chapter 8 * The Three-Level Unconditional Random Intercept Model with Longitudinal Data Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 9 * Repeated Measures and Heterogeneous Variance Models Overview SPSS SAS Stata R HLM 7 Summary Glossary Challenge Questions With Answers Chapter 10 * Residual and Influence Analysis for a Three-Level RC Model About This Chapter Overview Why Residual Analysis? Data Model Model Diagnostics SAS Stata SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 11 * Cross-Classified Linear Mixed Models Overview Data Model Research Purpose Stata SPSS SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 12 * Generalized Linear Mixed Models Overview Estimation Methods Data Model Stata SAS SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Appendix 1: Data Used in Examples. Refers to Student Companion Website Appendix 2: Reporting Multilevel Results References Index

"The practical and hands-on approach in addition to using several software make this book appealing to a wide range of readers." -- Amin Mousavi "This is a solid treatment of MLMs which illustrates implementation across all major MLM software." -- J.M. Pogodzinski "This text effectively balances depth, complexity, and readability of a number of challenging topics related to multilevel modeling. The wealth of examples in many different software environments are fantastic." -- Michael Broda