FACTOR ANALYSIS
What Is Factor Analysis?
A technique used to identify a small number of underlying “factors” that explain the patterns of correlation among a
larger set of observed variables.
 Data Reduction
Turns many variables into a few manageable factors
In short: it simplifies complex data by grouping related
variables together.
 Reveals Structure
Uncovers hidden relationships not obvious in raw data
 Simplifies Analysis
Cleaner input for further statistical modelling
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Two Approaches to Factor Analysis
Exploratory (EFA)
• Used when the grouping of variables is not known in
advance
• Lets the data reveal its own structure
• Best for early-stage, discovery-driven research
Confirmatory (CFA)
• Used when a theory about the grouping already exists
• Tests whether the data fits that expected structure
• Best for validating an existing model or hypothesis
How Factor Analysis Works
1. Collect Data
Gather responses across many related variables (e.g. a survey)
2. Build a Correlation Matrix
Identify which variables move together
3. Extract Factors
Commonly using Principal Component Analysis
4. Rotate Factors
Apply rotation (e.g. Varimax) to ease interpretation
5. Label & Interpret
Name each factor based on which variables load onto it
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Worked Example: Employee Satisfaction Survey
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20
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Work Environment
Management Support
Career Growth
Office space, noise, comfort
Feedback, communication,
recognition
Training, promotion, skill
development
Survey Questions
Result: 20 questions collapse into 3 underlying dimensions of employee satisfaction — easier to present and act on.
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Advantages
Data Reduction
Objective Basis
Simplifies large datasets into a few meaningful factors
Grounded in correlation, less researcher bias
Reveals Hidden Structure
Scales to Large Surveys
Surfaces relationships not obvious in raw data
Handles many related Likert-style items well
Improves Other Analyses
Cleaner inputs for regression, reduces multicollinearity
Best suited for: large-scale, quantitative survey research with many related variables.
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Disadvantages
Needs Large Samples
Technically Complex
Small datasets give unstable, unreliable factors
Requires statistical software and sound stats knowledge
Subjective Labelling
Doesn’t Explain “Why”
Naming what a factor “means” is still a judgement call
Shows what groups together, not the reasons behind it
Assumes Linear Relationships
Doesn’t capture non-linear patterns well
Complement it with qualitative methods to explain the reasons behind the patterns.
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How It Compares
Aspect
Factor Analysis
Qualitative Methods
Data type
Numerical (ratings, scores)
Text, interviews
Purpose
Reduce variables into factors
Find patterns in words
Output
Groups of related variables
Themes, narratives
Approach
Statistical, data-driven
Interpretive
Best for
Simplifying many variables
Understanding “why”
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Thank You