Best Practices When Using Factor Analysis

In real-world datasets, patterns often exist—yet the true reasons behind them aren’t always visible at first glance.
Take a simple demographic survey as an example:
Married men tend to spend more than single men
Married men with children spend even more
At first, this pattern looks simple. But the real driving factors may include:
Income level
Education
Location
Family size
These hidden drivers are hard to capture manually. Trying to force variables into predefined categories introduces bias, guesswork, and oversimplification.
This is exactly where Factor Analysis comes in.
Factor analysis offers a different lens—it automatically groups variables into latent (hidden) factors and assigns weights based on how strongly each variable influences those factors.

Creating Meaningful Factors
Factor Analysis begins with the idea that:
✅ Hidden (latent) variables exist
✅ These variables influence observed responses
✅ We don’t directly observe them—but we can infer them mathematically
How this transformation works
Instead of removing information, we transform variables using:
Eigenvectors → directions of maximum variance
Eigenvalues → how much variance is explained
Key rule:
If an eigenvalue > 1, that factor explains more variance than a single variable.
Steps performed internally:
Transform original variables
Rank factors by explained variance
Optionally reduce dimensions
Usually retain factors explaining 90–99% variance
This removes the need to guess the number of factors.

Understanding Factor Loadings
Even after transformation, we retain the weights of original variables inside each factor.
These weights are known as:
Factor Loadings
They show how strongly a variable contributes to a factor.
Example (Airline Survey – Conceptual)
Imagine 10 survey variables → 10 factors.
From loadings:
FactorInterpretation
Factor 1
Customer experience
Factor 2
Booking & loyalty perks
Factor 3
Competitive advantage
If one variable loads negatively on a factor, it means it reduces its influence.
This interpretability is what makes factor analysis extremely powerful.

Exploratory vs Confirmatory Factor Analysis
Confirmatory Factor Analysis (CFA)
Used when:
You already know the structure
You want to validate assumptions
Exploratory Factor Analysis (EFA)
Used when:
Structure is unknown
Goal is data discovery
How do we choose the number of factors?
We use the Scree Plot, which:
Plots eigenvalues
Finds the “elbow point” where variance drops sharply
Chooses factors before this drop

Practical Walkthrough – Factor Analysis in R (BFI Dataset)
We’ll use the psych package and the built-in bfi dataset.
Step 1: Install and Load Package
install.packages("psych")
library(psych)

Load dataset

bfi_data = bfi

Step 2: Remove Missing Values
bfi_data = bfi_data[complete.cases(bfi_data),]

✅ Remaining rows: 2236

Step 3: Create Correlation Matrix
bfi_cor <- cor(bfi_data)

Step 4: Run Factor Analysis
factors_data <- fa(r = bfi_cor, nfactors = 6)

Step 5: View Loadings and Results
factors_data

Your output shows:
Factor loadings per variable
Variance explained
Fit statistics
Correlations among factors
Key Insight from Results
The factors align well with known personality dimensions:
Neuroticism (N) appeared strongest
Followed by:
Conscientiousness (C)
Extraversion (E)
Agreeableness (A)
Openness (O)
This confirms the structure built into the dataset.

Best Practices When Using Factor Analysis
When interpreting results:
✅ Loadings < 0.3 → Too many factors
✅ Loadings around 0.5 → Weak but usable
✅ Strong predictors → > 0.7
Red Flags to Watch
Very low loadings across all factors
Factors that are impossible to interpret
Too many or too few factors
Factor analysis can also be used to:
✅ Track behavioral shifts over time
✅ Detect data drift
✅ Reduce dimensions without losing meaning

Complete Code (As Used in This Article)
install.packages("psych")
library(psych)

Load dataset

bfi_data = bfi

Remove missing data

bfi_data = bfi_data[complete.cases(bfi_data),]

Correlation matrix

bfi_cor <- cor(bfi_data)

Factor analysis

factors_data <- fa(r = bfi_cor, nfactors = 6)

Display results

factors_data

Conclusion – A Deeper Lens Into Your Data
Factor analysis helps you:
✅ Uncover hidden structure
✅ Replace biased manual grouping
✅ Reduce features without losing meaning
✅ Improve model interpretability
The true strength of factor analysis lies not in computation, but in human interpretation of factor loadings.
If factors are:
Hard to explain → adjust factor count
Too weak → refine dataset
Too complex → retrain with different assumptions
At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. As a power bi development company and a trusted partner to hire power bi consultants, we deliver tailored solutions that turn data into strategic insight. We would love to talk to you—do reach out to us.