Factor Analysis and Dimension Reduction in R

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

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Data Frame;binary;Kernel PCA;Confirmatory factor analysis;Common Factor Analysis;data analysis;PC1 PC2;dimension reduction;Kaiser Criterion;factory analysis;Proportion Var;mixed data;Principal Axis Factoring;multivariate;Cumulative Var;multivariate statistics;PCA Model;ordinal;Oblique Rotation;PCA;Polychoric Correlation;ICA;Principal Components Analysis;Iris Data;quantitative methods;Missing Values;quantitative research;RC1 RC2 RC3 RC5 RC4;R;Bartlett Scores;R code;Scree Plot;RStudio;Ability Data;Functional PCA;Pattern Matrix;Factor Scores;SS Loading;Correlation Matrix;Factor Correlation Matrix