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- Broschiertes Buch
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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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
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 564
- Erscheinungstermin: 16. Dezember 2022
- Englisch
- Abmessung: 244mm x 175mm x 39mm
- Gewicht: 1082g
- ISBN-13: 9781032246697
- ISBN-10: 1032246693
- Artikelnr.: 65609839
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 564
- Erscheinungstermin: 16. Dezember 2022
- Englisch
- Abmessung: 244mm x 175mm x 39mm
- Gewicht: 1082g
- ISBN-13: 9781032246697
- ISBN-10: 1032246693
- Artikelnr.: 65609839
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
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
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
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