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Latent Variable Modeling with R (Hardcover)
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Latent Variable Modeling with R (Hardcover)
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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 statistical
details to help readers better understand the models and interpret
the results. Every R command 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 relevant 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 fitting nonlinear structural equation
models, nonparametric item response theory models, and mixture
regression models. -Demonstrates how data simulation can help
researchers better understand statistical methods 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. The book opens with basic
instructions in how to use R to read data, download functions, and
conduct basic analyses. From there, each chapter is dedicated to a
different latent variable model including exploratory and
confirmatory factor analysis (CFA), structural equation modeling
(SEM), multiple groups CFA/SEM, least squares estimation, growth
curve models, mixture models, item response theory (both
dichotomous and polytomous items), differential item functioning
(DIF), and correspondance analysis. The book concludes with a
discussion of how data simulation can be used to better understand
the workings of a statistical method and assist researchers in
deciding on the necessary sample size prior to collecting data. A
mixture of independently developed R code along with available
libraries for simulating latent models in R are provided so readers
can use these simulations to analyze data using the methods
introduced in the previous chapters. Intended for use in graduate
or advanced undergraduate courses in latent variable modeling,
factor analysis, structural equation modeling, item response
theory, measurement, or multivariate statistics taught in
psychology, education, human development, and social and health
sciences, researchers in these fields also appreciate this book's
practical approach. The book provides sufficient conceptual
background information to serve as a standalone text. Familiarity
with basic statistical concepts is assumed but basic knowledge of R
is not.
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