Exploratory Factor Analysis

Оглавление

W. Holmes Finch. Exploratory Factor Analysis

Exploratory Factor Analysis

Contents

Series Editor’s Introduction

About the Author

Acknowledgments

Chapter 1 Introduction to Factor Analysis

Latent and Observed Variables

The Importance of Theory in Doing Factor Analysis

Comparison of Exploratory and Confirmatory Factor Analysis

EFA and Other Multivariate Data Reduction Techniques

A Brief Word About Software

Outline of the Book

Chapter 2 Mathematical Underpinnings of Factor Analysis

Correlation and Covariance Matrices

The Common Factor Model

Correspondence Between the Factor Model and the Covariance Matrix

Eigenvalues

Error Variance and Communalities

Summary

Descriptions of Images and Figures

Chapter 3 Methods of Factor Extraction in Exploratory Factor Analysis

Eigenvalues, Factor Loadings, and the Observed Correlation Matrix

Maximum Likelihood

Principal Axis Factoring

Principal Components Analysis

Principal Components Versus Factor Analysis

Other Factor Extraction Methods

Example

Summary

Chapter 4 Methods of Factor Rotation

Simple Structure

Orthogonal Versus Oblique Rotation Methods

Common Orthogonal Rotations

Varimax Rotation

Quartimax Rotation

Equamax Rotation

Common Oblique Rotations

Promax Rotation

Oblimin

Geomin Rotation

Target Factor Rotation

Bifactor Rotation

Example

Deciding Which Rotation to Use

Summary

Appendix

Descriptions of Images and Figures

Chapter 5 Methods for Determining the Number of Factors to Retain in Exploratory Factor Analysis

Scree Plot and Eigenvalue Greater Than 1 Rule

Objective Methods Based on the Scree Plot

Eigenvalues and the Proportion of Variance Explained

Residual Correlation Matrix

Chi-Square Goodness of Fit Test for Maximum Likelihood

Parallel Analysis

Minimum Average Partial

Very Simple Structure

Example

Summary

Descriptions of Images and Figures

Chapter 6 Final Issues in Factor Analysis

Proper Reporting Practices for Factor Analysis

Factor Scores

Power Analysis and A Priori Sample Size Determination

Dealing With Missing Data

Exploratory Structural Equation Modeling

Multilevel EFA

Summary

References

Index

Отрывок из книги

Exploratory Factor Analysis

A SAGE PUBLICATIONS SERIES

.....

Dfi = wf1 x1i + wf2 x2i + ⋅⋅⋅ + wfp xpi (Equation 1.1)

For each of these discriminant functions (Df), there is a set of weights that are akin to regression coefficients and correlations between the observed variables and the functions. Interpretation of the DA results usually involves an examination of these correlations. An observed variable having a large correlation with a discriminant function is said to be associated with that function in much the same way that indicator variables with large loadings are said to be associated with a particular factor. Quite frequently, DA is used as a follow-up procedure to a statistically significant multivariate analysis of variance (MANOVA). Variables associated with discriminant functions with statistically significantly different means among the groups can be concluded to contribute to the group mean difference associated with that function. In this way, the functions can be characterized just as factors are, by considering the variables that are most strongly associated with them.

.....