How to do Factor Analysis (Factor Analysis Steps)?
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Here are the general steps involved in conducting a factor analysis:
1. Determine the Suitability of Data for Factor Analysis
- Bartlett’s Test: Check the significance level to determine if the correlation matrix is suitable for factor analysis.
- Kaiser-Meyer-Olkin (KMO) Measure: Verify the sampling adequacy. A value greater than 0.6 is generally considered acceptable.
2. Choose the Extraction Method
- Principal Component Analysis (PCA): Used when the main goal is data reduction.
- Principal Axis Factoring (PAF): Used when the main goal is to identify underlying factors.
3. Factor Extraction
- Use the chosen extraction method to identify the initial factors.
- Extract eigenvalues to determine the number of factors to retain. Factors with eigenvalues greater than 1 are typically retained in the analysis.
- Compute the initial factor loadings.
4. Determine the Number of Factors to Retain
- Scree Plot: Plot the eigenvalues in descending order to visualize the point where the plot levels off (the “elbow”) to determine the number of factors to retain.
- Eigenvalues: Retain factors with eigenvalues greater than 1.
5. Factor Rotation
- Orthogonal Rotation (Varimax, Quartimax): Assumes that the factors are uncorrelated.
- Oblique Rotation (Promax, Oblimin): Allows the factors to be correlated.
- Rotate the factors to achieve a simpler and more interpretable factor structure.
- Examine the rotated factor loadings.
6. Interpret and Label the Factors
- Analyze the rotated factor loadings to interpret the underlying meaning of each factor.
- Assign meaningful labels to each factor based on the variables with high loadings on that factor.
7. Compute Factor Scores (if needed)
- Calculate the factor scores for each individual to represent their value on each factor.
8. Report and Validate the Results
- Report the final factor structure, including factor loadings and communalities.
- Validate the results using additional data or by conducting a confirmatory factor analysis if necessary.
Factor Analysis | Data Analysis
Factor analysis is a statistical method used to analyze the relationships among a set of observed variables by explaining the correlations or covariances between them in terms of a smaller number of unobserved variables called factors.
Table of Content
- What is Factor Analysis?
- What does Factor mean in Factor Analysis?
- How to do Factor Analysis (Factor Analysis Steps)?
- Factor Analysis Example (Factor Analyzer):
- Why do we need factor analysis?
- Most Commonly used Terms in Factor Analysis
- Types of Factor Analysis
- Types of Factor Extraction Methods
- Assumptions of Factor Analysis
- FAQs : Factor analysis