Advances in Multilevel Modeling for Educational Research

Jeffrey R. Harring, University of Maryland, USALaura M. Stapleton, University of Maryland, USAS. Natasha Beretvas, The University of Texas at Austin, USA

• Foreword, Sophia Rabe-Hesketh.Preface, Jeffrey R. Harring, Laura M. Stapleton, and S. Natasha Beretvas.Acknowledgements.Part I: Methodological Issues in Multilevel Models.Chapter 1. The Discrepancy Between Measurement and Modeling in Longitudinal Data Analysis, Daniel J. Bauer and Patrick J. Curran.Chapter 2. Incomplete Multilevel Data: Problems and Solutions, Joop Hox, Stef van Buuren, and Shahab Jolani.Chapter 3. Sampling Weight Considerations for Multilevel Modeling of Panel Data, Laura M. Stapleton, Jeffrey R. Harring, and Daniel Y. Lee.Chapter 4. Residual Diagnostics and Model Assessment in a Multilevel Framework: Recommendations Toward Best Practice, Ann A. O'Connell, Gloria Yeomans-Maldonado, and D. Betsy McCoach.Part II: Handling Longitudinal Data Complexities: Crossclassified and Random Effects Latent Variable Models.Chapter 5. Multilevel Cross-Classified Testlet Model for Complex Item and Person Clustering in Item Response Data Analysis, Hong Jiao, Akihito Kamata, and Chao Xie.Chapter 6. General Random Effect Latent Variable Modeling: Random Subjects, Items, Contexts, and Parameters, Tihomir Asparouhov and Bengt Muthén.Chapter 7. N-Level Structural Equation Model of Student Achievement Data Nested with Repeated Teachers, Schools, and Districts, Paras D. Mehta and Yaacov Petscher.Chapter 8. A Model for Cross-Classified Nested Repeated Measures Data, Jeffrey R. Harring, S. Natasha Beretvas, and Anita Israni.Chapter 9. Cross-Classified Random Effects Models for Assessing Rater Severity and Differential Rater Functioning, S. Natasha Beretvas, Daniel L. Murphy, and Matthew N. Gaertner.Part III. Causal Inference and Advanced Psychometric Models.Chapter 10. Handling Measurement Error in Predictors With a Multilevel Latent Variable: Plausible Values Approach, Ji Seung Yang and Michael Seltzer.Chapter 11. Mixture Modeling Methods for Causal Inference With Multilevel Data, Jee-Seon Kim, Peter M. Steiner, and Wen-Chiang Lim.Chapter 12. Multilevel Social Network Models: Incorporating Network-Level Covariates Into Hierarchical Latent Space Models, Tracy Sweet and Qiwen Zheng.Biographies.