Exploratory Factor Analysis
Holmes Finch
- Ball State University, USA
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Go to College Publishing WebsiteDescription
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.
Contents
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
Additional materials
Description
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.
Contents
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
Additional materials
Reviews
September 2019 | 144 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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 Contents:
- 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
Recent Product Reviews:
This text is a perfect resource for individuals seeking guidance on applied factor analysis, covering the fundamentals as well as introductions to more advanced aspects of factor analytic techniques.
Damon Cann, Utah State University
Finch provides a well-written and well-organized introduction to the conceptual and quantitative topics of exploratory and confirmatory factor analysis within a single, concise text.
Stephen G. Sapp, Iowa State University
This is a thorough and readable introduction to exploratory factor analysis
Michael D. Biderman, University of Tennessee at Chattanooga