Dimensionality Reduction & Factor Analysis in SPSS
Application of PCA and exploratory factor analysis to identify latent dimensions and interpret patterns in business datasets.
Type
Academic work
Area
Multivariate Analysis · Market Research
Tools
SPSS
Techniques
PCA · Exploratory factor analysis · KMO · Bartlett test · Communalities · Factor rotation
Output
Interpreted multivariate analysis
Value
Academic work where I applied PCA and exploratory factor analysis in SPSS to identify latent dimensions, reduce variables, and interpret useful patterns for market research and business analysis.
Executive summary
Methodological case useful for market research: turning many observed variables into interpretable dimensions without losing business reading.
Business context
Surveys and business studies often include many correlated variables. Dimensionality reduction helps synthesise information, build scales, and detect latent patterns.
My role
Individual academic work. I ran the analysis in SPSS, reviewed factor adequacy, and interpreted components/factors with a market-research orientation.
Data & methods
- SPSS and .spv outputs.
- Principal component analysis and exploratory factor analysis.
- Evaluation through KMO, Bartlett, communalities, and rotation.
Process
- 01Review the correlation matrix.
- 02Evaluate suitability for factor analysis.
- 03Extract components/factors.
- 04Apply rotation and interpret loadings.
- 05Translate dimensions into business reading.
Key findings
- The technique condenses information and detects latent dimensions.
- Interpretation depends on variable quality and correlation structure.
Business implications
- Transferable to segmentation, market research, perception scales, and customer insights.
- Complements consumer-insight projects with many attitudinal variables.
Limitations
- Academic work with practice data.
- Not presented as a real business-impact case.
What I would do next
- Apply it to primary surveys with segmentation goals.
- Connect factors with predictive models or customer profiles.
Assets
Suggested visuals
Factor loading table.
Scree plot.
Latent dimensions map.
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