Factor Analysis Paper Guide | Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA)
How to write a factor analysis paper? AcademicIdeas covers EFA and CFA: KMO and Bartlett tests, factor rotation methods, and standard reporting formats for management research.
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How to write a factor analysis paper? AcademicIdeas covers EFA and CFA: KMO and Bartlett tests, factor rotation methods, and standard reporting formats for management research.
- Understand when to use EFA vs CFA
- Learn KMO/Bartlett testing and factor rotation methods
- Master standard reporting format for factor analysis
- Factor analysis summarizes interrelated variables into a few uncorrelated latent factors.
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Reviewed against the public research-method page, SPSS guide, and SEM page, then cross-checked with IBM SPSS documentation and UCLA IDRE materials so this support page stays aligned on EFA/CFA positioning, KMO and Bartlett checks, rotation choices, and reporting conventions.
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What this page helps you do first
- Understand when to use EFA vs CFA
- Learn KMO/Bartlett testing and factor rotation methods
- Master standard reporting format for factor analysis
Essence of factor analysis: dimensionality reduction
Factor analysis summarizes interrelated variables into a few uncorrelated latent factors. It is commonly divided into Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), which serve fundamentally different purposes and applications.
EFA vs CFA: key differences
- [EFA] Explores potential data structure without prior theory — purely data-driven factor structure discovery
- [CFA] Tests pre-specified factor structure based on prior theory — requires theoretical justification
- [Choice principle] New scales: EFA first, then CFA; established scales: CFA directly; dimensionality reduction: PCA
Suitability testing and factor extraction
- [KMO test] >0.7 suitable; 0.6-0.7 marginal; <0.6 unsuitable
- [Bartlett test] p<0.05 indicates correlations exist, suitable for factor analysis
- [Eigenvalue] Factors with eigenvalue>1 retained (Kaiser criterion)
- [Cumulative variance] Generally require >60% cumulative variance explained
Factor rotation: purpose and method selection
- [Why rotate] Initial factor loadings are often difficult to interpret — rotation simplifies interpretation
- [Varimax (orthogonal)] Most common, forces factors to be uncorrelated
- [Promax (oblique)] Allows factors to correlate — more realistic but complex interpretation
Standard reporting format
- Sample size, KMO value, Bartlett test results
- Number of factors extracted, eigenvalues, variance explained
- Rotated factor loading matrix (table format)
- Cronbach's α for each factor
- Factor scores correlation matrix if used in regression
Frequently asked questions
- What is the difference between factor analysis and PCA?
- Factor analysis is based on latent factor models; PCA is pure linear transformation. Management questionnaire research typically uses factor analysis for latent variables; PCA for dimensionality reduction.
- What if KMO is only 0.6?
- KMO between 0.6-0.7 is marginal. Try: delete low-correlation variables; increase sample size; or consider alternative methods. Below 0.6 makes factor analysis unreliable.