A correlation matrix displays Pearson correlation coefficients (r) between all pairs of variables in a dataset. It helps identify multicollinearity, explore patterns, and guide further statistical modelling.
Step-by-step instructions for common statistical software packages
Navigate to Analyze → Correlate → Bivariate from the main SPSS menu bar.
Select all continuous variables and move them to the Variables box on the right side.
Ensure Pearson is checked as correlation coefficient. Tick "Flag significant correlations" to show asterisks.
Review the correlation matrix in the Output window. Look for asterisks indicating p < .05, .01, or .001.
Install and load corrplot for visualisation and psych for significance testing.
Use the cor() function with pairwise deletion for missing data.
Create a colour-coded heatmap with correlation coefficients displayed.
Use corr.test() to obtain p-values for each correlation coefficient.
Go to File → Options → Add-ins → Manage Excel Add-ins → check Analysis ToolPak → OK.
Click Data tab → Data Analysis → Correlation → OK to open the dialog box.
Select the cell range containing your variables. Check "Labels in first row" if headers are included.
Apply a 3-color scale conditional format (Home → Conditional Formatting → Color Scales) to visualise correlation heatmap.
Visual examples of different correlation strengths and directions
As one variable increases, the other consistently increases. Points cluster tightly around an upward-sloping line.
A clear upward trend exists but with more scatter. The relationship is visible but not perfect.
As one variable increases, the other tends to decrease. The downward trend is discernible but scattered.
No systematic relationship between variables. Points appear randomly scattered with no trend.