Latent variable modeling with R

"This text demonstrates how to conduct latent variable modeling in R. Techniques that can be analyzed using the free program R are showcased including exploratory and confirmatory factor analysis, structural equation modeling (SEM), latent growth curve modeling, item response theory (IRT), and latent class analysis. Easy to follow demonstrations of how to conduct latent variable modeling in R are provided along with descriptions of the major features of the models, their specialized uses, and a full interpretation of the results. Every R command necessary for conducting the analyses is described so readers can directly apply the R functions to their own data. Each chapter features a complete analysis of one or more example datasets including a demonstration of the analysis of the data using R, along with a discussion of relevant theory that includes a full description of the models, the assumptions underlying each model, and statistical details of estimation, hypothesis testing, and more to help readers better understand the models and interpret the results. Some of the examples represent data that is not perfectly "behaved" so as to provide a more realistic view of situations readers will likely encounter with their own data. Detailed explanations of input statements help readers generalize what they learn to their own analyses. Each chapter features an introduction, summary, and exercises involving the application of the model(s), and a list of further readings with an emphasis on related texts that provide more detailed theoretical coverage. A full glossary of the key terms, a cheat sheet that reviews the key R commands, and answers to half of the exercises are provided at the end of the book"--

Versement en lot.

Cover; Title Page; Copyright Page; Dedication; Table of Contents; Preface; Acknowledgments; About the authors; Chapter 1: Introduction to basic data handling in R; Introduction; The R console and R scripts; R libraries; Reading data into R; Missing data; Types of data; R Commander and RStudio; Summary; Further reading; References; Chapter 2: Exploratory factor analysis; Introduction; Exploratory factor analysis; Factor extraction; Factor rotation; Statistical methods for determining the optimal number of factors; Fitting EFA models using factanal; Fitting EFA models using fa.

Principal components analysisSummary; Further reading; References; Chapter 3: Confirmatory factor analysis; Introduction; Model parameter estimation; Assessing model fit; Fitting CFA models in R using lavaan; Summary; Further reading; References; Chapter 4: Foundations of structural equation modeling; Introduction; The importance of substantive theory in SEM; Fitting the measurement model in R; Fitting the structural model in R; Fitting alternative SEMs; Summary; Further reading; References; Chapter 5: SEM for multiple groups, the MIMIC model, and latent means comparisons; Introduction.

Multiple groups SEMS…