Latent VariabLe
ModeLingwithr
This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting
the features of each model, their specialized uses, examples, sample code and output, and an
interpretation of the results. Each chapter features a detailed example including the analysis of
the data using R, the relevant theory, the assumptions underlying the model, and other statis-
tical details to help readers better understand the models and interpret the results. Every R com-
mand necessary for conducting the analyses is described along with the resulting output which
provides readers with a template to follow when they apply the methods to their own data. The
basic information pertinent to each model, the newest developments in these areas, and the rele-
vant R code to use them are reviewed. Each chapter also features an introduction, summary,
and suggested readings. A glossary of the text’s boldfaced key terms and key R commands serve
as helpful resources. The book is accompanied by a website with exercises, an answer key, and
the in-text example data sets.
Latent Variable Modeling with R:
• Provides some examples that use messy data providing a more realistic situation readers
will encounter with their own data.
• Reviews a wide range of LVMs including factor analysis, structural equation modeling,
item response theory, and mixture models and advanced topics such as tting nonlinear
structural equation models, nonparametric item response theory models, and mixture
regression models.
• Demonstrates how data simulation can help researchers better understand statistical meth-
ods and assist in selecting the necessary sample size prior to collecting data.
• www.routledge.com/9780415832458 provides exercises that apply the models along with
annotated R output answer keys and the data that corresponds to the in-text examples so
readers can replicate the results and check their work.
Intended for use in graduate or advanced undergraduate courses in latent variable modeling,
factor analysis, structural equation modeling, item response theory, measurement, or multi-
variate statistics taught in psychology, education, human development, and social and health
sciences, researchers in these elds also appreciate this book’s practical approach. The book pro-
vides sufcient conceptual background information to serve as a standalone text. Familiarity
with basic statistical concepts is assumed but basic knowledge of R is not.
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.
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