Distill Complexity, Into Core Factors
CFR Solutions runs factor analysis to reduce large sets of correlated survey variables into a handful of underlying factors — extracting eigenvalues, rotating loadings, and validating sampling adequacy so the drivers behind your data are clear and actionable.
Every Factor Extracted, Every Loading Validated
Sampling adequacy is checked with KMO and Bartlett's test before any factor is extracted, eigenvalues and scree plots decide how many factors to retain, and rotated loadings are cross-checked against theory so each factor has a clear, interpretable meaning.
Reduction Formats Matched to Your Dataset
From a first-pass exploratory scan to a fully confirmatory model, we size the factor solution around the variables and theory you bring us.
Exploratory Factor Analysis
Underlying factors uncovered from a large variable set with no prior structure assumed.
Learn more →Confirmatory Factor Analysis
A pre-specified factor structure tested for model fit against your hypothesised theory.
Learn more →Varimax & Oblique Rotation
Loadings rotated for maximum interpretability, choosing orthogonal or correlated factor solutions as needed.
Learn more →KMO & Bartlett's Test
Sampling adequacy and inter-correlation checked upfront, confirming the dataset is fit for factoring.
Learn more →Scree Plot & Eigenvalue Validation
Eigenvalues charted to justify exactly how many factors are worth retaining in the final solution.
Learn more →Factor Score Computation & Reporting
Per-respondent factor scores computed and delivered in a client-ready report with loading tables.
Learn more →Explore Our Other Essential Services
Every technique is built on the same rigorous, quota-clean data — pick the analysis that matches the question your study needs to answer.
Brand Mapping
Perceptual maps showing how brands sit relative to each other on key attributes.
Learn more →CHAID Analysis
Decision-tree segmentation that splits respondents by the strongest predictors.
Learn more →Cluster Analysis
Groups respondents into natural segments based on shared attitudes or behavior.
Learn more →Conjoint Analysis
Measures the trade-offs respondents make between product features and price.
Learn more →Correlation Analysis
Measures how strongly two or more variables move together.
Learn more →Discriminant Analysis
Classifies respondents into known groups using their strongest predictors.
Learn more →Factor Analysis
Reduces a long list of variables down to the underlying factors driving them.
Learn more →Multidimensional Scaling
Visual maps of how respondents perceive similarity between items or brands.
Learn more →Regression Analysis
Quantifies how much each predictor moves the outcome you care about.
Learn more →