MLM Book: Interested in multilevel modeling, linear mixed models, or hierarchical linear modeling?
Please take a look at my new book now available from Sage Publications. Review copies are available for instructors. "Multilevel Modeling: Applications in STATA®, IBM® SPSS®,SAS®, R & HLM™" (2020) provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences.
Author G.David Garson’s step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource fo6 rtheir students and a foundation for their teaching alike. The book web page at Sage Publications is:https://us.sagepub.com/en-us/nam/multilevel-modeling/book260705 The flyer with more information and reviews is at: https://us.sagepub.com/en-us/nam/node/1531242/download-pdf
Draft Table of Contents:
1. Structural Equation Modeling: Jumping Right In 2. Structural Equation Modeling: Concepts and Terms 3. Regression-Based Path Analysis: A Precursor to SEM 4. Model Development and Evaluation 5. R Software for Structural Equation Modeling 6. The Confirmatory Factor Analysis Model (CFA) 7. The Structural Equation Model (SEM) 8. Multigroup Analysis in SEM (MGA) 9. Multilevel and Hierarchical SEM (MSEM and HSEM) 10. Latent Growth Curve Analysis (LGCA) 11. Mean Structure Analysis (MSA) 12. Generalized SEM (GSEM), Nonlinear SEM (NLSEM), and Mixture Models (SEMM) 13. Bayesian SEM (BSEM) 14. Other Models I Non-recursive SEM Exploratory SEM (ESEM) Bootstrapped SEM Piecewise SEM SEM Tree and Forest Models Structural After Measurement Analysis Models (SAM) 15. Other Mode1s II Latent Interaction Analysis (LIA) SEM Time Series Models Spatially Explicit Structural Equation Modeling (SESEM) SEM for Dyadic Data Actor-partner interdependence models (APIM) Latent traits-state occasion models 16. SEM Visualization Tools 17. Assumptions of SEM 18. APPENDIX I. DATASETS USED IN THIS VOLUME 19. APPENDIX 2. INTRODUCTION TO R AND RSTUDIO
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