Description
Book SynopsisFactor 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, regulariz
Table of Contents
PART I: MULTIVARIATE ANALYSIS OF FACTORS AND COMPONENTS
Chapter 1: Factor Analysis: Purposes and Research Questions
Chapter 2: Dealing with the Assumptions and Limitations of Factor Analysis
Chapter 3: Fundamental Concepts and Functions in Factor Analysis
Chapter 4: Quick Start: Principal Axis Factoring (FA) in R
Chapter 5: Quick Start: Confirmatory Factor Analysis in R
Chapter 6. Quick Start: Principal Components Analysis (PCA) in R
Chapter 7: Oblique and Higher Order Factor Models
Chapter 8: Factor Analysis for Binary, Ordinal, and Mixed Data
Chapter 9: FA in Greater Detail
Chapter 10: PCA in Greater Detail
PART II: ADDITIONAL TOOLS FOR DIMENSION REDUCTION
Chapter 11: Sixteen Additional Methods for Dimension Reduction (DimRed)
Chapter 12: Metrics for Comparing and Evaluating Dimension Reduction Models
Chapter 13: Recipes: An Alternative System for Dimension Reduction
Chapter14: Factor Analysis for Neural Models
Chapter 15: Factor Analysis for Time Series Data
APPENDICES
I. Datasets used in this volume
2. Introduction to R and RStudio
