Holmes Finch 
Exploratory Factor Analysis 

A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences.
Exploratory Factor Analysis by W. Holmes Finch provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data.  It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.

Table of Content

Chapter One: 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 Two: 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

Chapter Three: 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 Four: Methods of Factor Rotation

Simple Structure

Orthogonal Versus Oblique Rotation Methods

Common Orthogonal Rotations

Common Oblique Rotations

Target Factor Rotation

Bifactor Rotation

Example

Deciding Which Rotation to Use

Summary

Appendix

Chapter Five: 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

Chapter Six: 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

About the author

W. Holmes Finch (Ph.D. South Carolina) is the George and Frances Ball Distinguished Professor of Educational Psychology in the Department of Educational Psychology at Ball State University. Prior to coming to Ball State, he worked as a consultant in the Statistics Department at the University of South Carolina for 12 years advising faculty and graduate students on the appropriate statistical methods for their research. Dr. Finch teaches courses in statistical and research methodology as well as psychometrics and educational measurement. His research interests involve issues in psychometrics, including dimensionality assessment, differential item functioning, generalizability theory and unfolding models. In addition, he pursues research in multivariate statistics, particularly those involving nonparametric techniques. He is the co-author of Multilevel Modeling Using R (with Holden, J.E., & Kelley, K., CRC Press, 2014); Applied Psychometrics Using SAS (with French, B.F. & Immekus, J., Information Age, 2014); and Latent Variable Models in R (with French, B.F., Routledge, 2015).