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Link to Multilevel Modeling page

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



Link to Data Analytics for the Social Sciences: Applications in R page.

Look for my newest book, from Routledge Publishers (2022). Note; Data and most supplementary readings and other materials are under the "Students" tab.
TABLE OF CONTENTS
CHAPTER ONE: USING AND ABUSING DATA ANALYTICS IN SOCIAL SCIENCE
CHAPTER TWO: STATISTICAL ANALYTICS WITH R, PART 1
CHAPTER THREE: STATISTICAL ANALYTICS WITH R, PART 2
CHAPTER FOUR: CLASSIFICATION AND REGRESSION TREES
CHAPTER FIVE: RANDOM FORESTS
CHAPTER SIX: MACHINE LEARNING MODELS WITH R
CHAPTER SEVEN: NEURAL NETWORK MODELS AND DEEP LEARNING
CHAPTER EIGHT: NETWORK ANALYSIS
CHAPTER NINE: TEXT ANALYTICS
APPENDIX 1: GETTING START WITH R
APPENDIX 2: DATA USED IN THIS BOOK



Factor Analysis and Dimension Reduction in R will be published by Routledge in late 2022 or early 2023.
By G. David Garson, North Carolina State University

In addition to covering principal components analysis and principal factor analysis with orthogonal and oblique rotation, this book covers such advanced topics as higher order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. In production with Routledge Publishing and expected to be published in late 2022 with a 2023 publication date. The book has 15 chapters and contains 144 original figures.


Structural Equation Modeling with R. In progress. Not yet available. Expected publication: late 2023 or early 2024.

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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