This step-by-step guide is written for R and latent variable model
(LVM) novices. Utilizing a path model approach and focusing on the
lavaan package, this book is designed to help readers quickly
understand LVMs and their analysis in R. The author reviews the
reasoning behind the syntax selected and provides examples that
demonstrate how to analyze data for a variety of LVMs. Featuring
examples applicable to psychology, education, business, and other
social and health sciences, minimal text is devoted to theoretical
underpinnings. The material is presented without the use of matrix
algebra. As a whole the book prepares readers to write about and
interpret LVM results they obtain in R. Each chapter features
background information, boldfaced key terms defined in the
glossary, detailed interpretations of R output, descriptions of how
to write the analysis of results for publication, a summary, R
based practice exercises (with solutions included in the back of
the book), and references and related readings. Margin notes help
readers better understand LVMs and write their own R syntax.
Examples using data from published work across a variety of
disciplines demonstrate how to use R syntax for analyzing and
interpreting results. R functions, syntax, and the corresponding
results appear in gray boxes to help readers quickly locate this
material. A unique index helps readers quickly locate R functions,
packages, and datasets. The book and accompanying website at
http://blogs.baylor.edu/rlatentvariable/ provides all of the data
for the book's examples and exercises as well as R syntax so
readers can replicate the analyses. The book reviews how to enter
the data into R, specify the LVMs, and obtain and interpret the
estimated parameter values. The book opens with the fundamentals of
using R including how to download the program, use functions, and
enter and manipulate data. Chapters 2 and 3 introduce and then
extend path models to include latent variables. Chapter 4 shows
readers how to analyze a latent variable model with data from more
than one group, while Chapter 5 shows how to analyze a latent
variable model with data from more than one time period. Chapter 6
demonstrates the analysis of dichotomous variables, while Chapter 7
demonstrates how to analyze LVMs with missing data. Chapter 8
focuses on sample size determination using Monte Carlo methods,
which can be used with a wide range of statistical models and
account for missing data. The final chapter examines hierarchical
LVMs, demonstrating both higher-order and bi-factor approaches. The
book concludes with three Appendices: a review of common measures
of model fit including their formulae and interpretation; syntax
for other R latent variable models packages; and solutions for each
chapter's exercises. Intended as a supplementary text for graduate
and/or advanced undergraduate courses on latent variable modeling,
factor analysis, structural equation modeling, item response
theory, measurement, or multivariate statistics taught in
psychology, education, human development, business, economics, and
social and health sciences, this book also appeals to researchers
in these fields. Prerequisites include familiarity with basic
statistical concepts, but knowledge of R is not assumed.
General
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