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Latent Variable Modeling with R
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Table of Contents

1. Introduction to basic data handling in R 2. Exploratory Factor analysis 3. Confirmatory Factor analysis 4. Foundations of Structural Equation Modeling (SEM) 5. SEM for multiple groups, the MIMIC model, and latent means comparisons 6. Further Topics in SEM 7. Growth Curve Modeling 8. Mixture models 9. Item Response Theory for dichotomous and polytomous items 10. Further topics in Item Response Theory 11. Data simulation for latent variable modeling in R Appendix A. Key R Commands

About the Author

Brian F. French is a Professor of Measurement, Statistics, and Research Methods at Washington State University. W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology and Professor of Statistics and Psychometrics at Ball State University.

Reviews

"Finch and French provide a timely, accessible, and integrated resource on using R to fit a broad range of latent variable models. It will be a valuable reference for researchers as well as students taking SEM, IRT, Factor Analysis, or Mixture Modeling courses. Coverage of simulation methods and advanced topics in IRT and SEM are particular assets." - Sonya Sterba, Vanderbilt University, USA "With this highly accessible, easy-to-follow, step-by-step guide for analyzing both simple and complex latent variable models in the increasingly popular R software program, Finch and French provide an extremely valuable service to researchers in various fields." - Christian Geiser, Utah State University, USA "A major characteristic of this book is the user-friendly presentation of content and easy to follow examples. It provides excellent instruction to gain basic proficiency in the methods and reaches a large audience." - Karl Schweizer, Goethe University, Germany "The book integrates technical details and examples in a way that is friendly to beginner users of structural equation modeling and item response theory, and helps readers assimilate the concepts and transfer to their own research needs." - Walter L. Leite, University of Florida, USA "A cohesive and accessible resource for applying the R language to analyze data in a latent variable framework. ... I found what was written to be easy to understand. ... I would use it, recommend it to others, and likely adopt it as a supplemental text." - Natalie D. Eggum, Arizona State University, USA "This book is truly unique. ...The writing style is clear and comprehensible. ... [It] will ... serve as a ... supplementary text in ... measurement, latent variable modeling, and IRT classes." - D. Betsy McCoach, University of Connecticut, USA "This ... book covers all the latent variable models commonly used in social sciences. ... [It] can be used as a text ... for ... courses ... in ... structural equation modelling, psychological measurement, item response theory, and mixture model or latent class analysis. ... [It] makes a significant contribution to the field. ... I will use [it] as a supplement ... and recommend it to my colleagues who ... teach Psychological Measurement and Item Response Theory."- Ke-Hai Yuan, University of Notre Dame, USA "The topic has the potential ... to be of broad interest in social, prevention, and public health sciences. ... [It] could be of great interest to ... my students in courses on latent variable modeling." - Patrick S. Malone, University of South Carolina, USA

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