Quantifying What Moves Together
CFR Solutions applies Pearson, Spearman, and partial correlation techniques to your survey data to measure how strongly variables move together, flag multicollinearity, and surface the relationships that actually explain your outcome metric.
Every Relationship Measured, Every Driver Identified
Coefficients are computed against the appropriate method for your data type, every result is tested for statistical significance before it's reported, and variables that overlap too closely are flagged so your model stays clean and interpretable.
Correlation Techniques Matched to Your Data
From a simple two-variable check to a full driver analysis across your questionnaire, we choose the method that fits your data and your question.
Pearson Correlation Analysis
Linear correlation coefficients for continuous, normally distributed survey variables.
Learn more →Spearman Rank Correlation
Rank-based correlation for ordinal scales, Likert data, and non-linear relationships.
Learn more →Correlation Matrix Mapping
Full variable-by-variable matrices with heatmap visualization for quick pattern reading.
Learn more →Key Driver Analysis
Ranking the variables most strongly correlated with satisfaction, intent, or your KPI of choice.
Learn more →Multicollinearity Diagnostics
Flags variable pairs that overlap too closely, protecting downstream regression models.
Learn more →Partial Correlation Analysis
Isolates the relationship between two variables while controlling for a third confounding factor.
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 →