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.
01 / Fast scan
Case in 60 seconds
A quick scan of the case: what was happening, what needed to be solved, what I did, and what value is demonstrated.
Situation
SSurveys and business studies often include many correlated variables. Dimensionality reduction helps synthesise information, build scales, and detect latent patterns.
Task
TAcademic 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.
Action
A- 01Review the correlation matrix.
- 02Evaluate suitability for factor analysis.
- 03Extract components/factors.
- 04Apply rotation and interpret loadings.
Result
R- The technique condenses information and detects latent dimensions.
- Interpretation depends on variable quality and correlation structure.
- Transferable to segmentation, market research, perception scales, and customer insights.
- Complements consumer-insight projects with many attitudinal variables.
02 / Context
Problem
This section explains the business or analytical challenge before going into technical detail.
02.1
Executive summary
Methodological case useful for market research: turning many observed variables into interpretable dimensions without losing business reading.
02.2
My role
Individual academic work. I ran the analysis in SPSS, reviewed factor adequacy, and interpreted components/factors with a market-research orientation.
03 / Method
Approach
Methods, tools, and workflow. This shows how I structured the analysis.
03.1
Data & methods
- SPSS and .spv outputs.
- Principal component analysis and exploratory factor analysis.
- Evaluation through KMO, Bartlett, communalities, and rotation.
03.2
Process
- 01Review the correlation matrix.
- 02Evaluate suitability for factor analysis.
- 03Extract components/factors.
- 04Apply rotation and interpret loadings.
- 05Translate dimensions into business reading.
04 / Decision
Evidence and impact
Outputs, findings, and implications translated into decisions or professional value.
04.1
Key findings
- The technique condenses information and detects latent dimensions.
- Interpretation depends on variable quality and correlation structure.
04.2
Business implications
- Transferable to segmentation, market research, perception scales, and customer insights.
- Complements consumer-insight projects with many attitudinal variables.
05 / Close
Professional close
Limitations, next steps, and available assets. This keeps the case honest and actionable.
05.1
Limitations
- Academic work with practice data.
- Not presented as a real business-impact case.
05.2
What I would do next
- Apply it to primary surveys with segmentation goals.
- Connect factors with predictive models or customer profiles.
05.3
Assets
Keep reading
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