G. David Garson

Factor Analysis and Dimension Reduction in R (eBook, PDF)

A Social Scientist's Toolkit

• Format: PDF

Produktbeschreibung

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. This book will be suitable for graduate level and optional module courses for social scientists

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 584
• Erscheinungstermin: 16. Dezember 2022
• Englisch
• ISBN-13: 9781000810554
• Artikelnr.: 66258909

Autorenporträt

G. David Garson is Professor Emeritus in the School of Public and International Affairs, NCSU, specializing in advanced research methodology. His most recent works are Data Analytics for the Social Sciences: Applications in R (Routledge, 2022) and Multilevel Modeling: Applications in STATA, IBM, SPSS, SAS, R & HLM (Sage, 2020).

Inhaltsangabe

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