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Books > Reference & Interdisciplinary > Communication studies > Research methods
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Factor Analysis and Dimension Reduction in R - A Social Scientist's Toolkit (Paperback)
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Factor Analysis and Dimension Reduction in R - A Social Scientist's Toolkit (Paperback)
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Factor Analysis and Dimension Reduction in R provides coverage,
with worked examples, of a large number of dimension reduction
procedures along with model performance metrics to compare them.
Factor analysis in the form of principal components analysis (PCA)
or principal factor analysis (PFA) is familiar to most social
scientists. However, what is less familiar is understanding that
factor analysis is a subset of the more general statistical family
of dimension reduction methods. The social scientist's toolkit for
factor analysis problems can be expanded to include the range of
solutions this book presents. In addition to covering FA and PCA
with orthogonal and oblique rotation, this book's coverage includes
higher-order factor models, bifactor models, models based on binary
and ordinal data, models based on mixed data, generalized low-rank
models, cluster analysis with GLRM, models involving supplemental
variables or observations, Bayesian factor analysis, regularized
factor analysis, testing for unidimensionality, and prediction with
factor scores. The second half of the book deals with other
procedures for dimension reduction. These include coverage of
kernel PCA, factor analysis with multidimensional scaling, locally
linear embedding models, Laplacian eigenmaps, diffusion maps, force
directed methods, t-distributed stochastic neighbor embedding,
independent component analysis (ICA), dimensionality reduction via
regression (DRR), non-negative matrix factorization (NNMF), Isomap,
Autoencoder, uniform manifold approximation and projection (UMAP)
models, neural network models, and longitudinal factor analysis
models. In addition, a special chapter covers metrics for comparing
model performance. Features of this book include: Numerous worked
examples with replicable R code Explicit comprehensive coverage of
data assumptions Adaptation of factor methods to binary, ordinal,
and categorical data Residual and outlier analysis Visualization of
factor results Final chapters that treat integration of factor
analysis with neural network and time series methods Presented in
color with R code and introduction to R and RStudio, this book will
be suitable for graduate-level and optional module courses for
social scientists, and on quantitative methods and multivariate
statistics courses.
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