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"The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data."--Publisher's description

"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"--

Preface -- Introduction -- Data objects in R -- Data handling in R -- R programming & control flow -- Data exploration -- Graphics in R -- Regression analysis-1 -- Regression analysis-ii -- Time series analysis -- Extreme value theory modelling -- Introduction to multivariate analysis using copulas -- Bibliography -- Index