When it comes to analyzing data in crosstabs, metrics like Pearson's correlation coefficient can provide invaluable insights into the statistical relationships between variables in rows and columns. While the affinity index and statistical significance are commonly used in cross-tabulation, Pearson's correlation coefficient, often referred to as 'Correl', goes a step further by quantifying the strength of these relationships.
Interpreting the CORREL metric
Closer to 1 = strong positive statistical link.
When Correl approaches 1, it signifies a strong positive statistical link. In other words, when one variable increases, the other tends to increase as well. This implies a significant correlation between the two variables.
Closer to -1 = strong negative statistical link.
When Correl approaches -1, it indicates a strong negative statistical link. As one variable increases, the other tends to decrease, suggesting a significant inverse correlation.
Closer to 0 = weak statistical link.
When Correl is close to 0, it suggests a weak statistical link between the variables. This means that there is little to no linear relationship between the variables, making it difficult to draw meaningful conclusions based solely on their correlation.
Here is an example of CORREL metrics
There is a positive statistical link between the consumption of Vector Cola and LiquiBoost and the male gender (value of Correl metric > 0).
However, for VectorCola the link is strong which means the consumption of VectorCola is more typical for men than the consumption of LiquiBoost (0.3480 > 0.0024).
Brands Fresh Story and Wellspring Fresh exhibit negative correlations with the male gender, suggesting that their consumption among men is less typical (value of Correl metric < 0).