Creating a correlation matrix for conjoint simulations
Creating a correlation matrix for your simulations is easy.
- Run your preference and revenue share simulations by inserting the product names, attributes and levels that you want.
- Click then
Now you can see your correlation matrix where the header corresponds to the name of products/attributes/levels combinations you inserted earlier.
You may also export your correlation matrix to
How to interpret correlation?
Pearson correlation coefficient,
ρ measures the degree of relationship between two variables. The range of correlation coefficient will be from
ρ = +1indicates strong relationship between two variables. Example: If respondents like Coca-Cola, they also tend to like Pepsi.
ρ = 0indicates that there is no relationship between two variables. Example: Price of an orange and price of a table have zero correlation with each other. If price of an orange increases, nothing can be inferred about the price of a table because they are not dependent on each other.
ρ = -1indicates that when one variable increases, the other variable decreases (moves in opposite direction). Example: As you climb a mountain (increase in height), the temperature decreases.
Take our Claims Test for Yogurt for example, we can see that:
- Claim 1 and claim 10 have high positive correlation (
ρ = 0.7). This suggests that respondents who like claim 1 tend to like claim 10 as well.
- Claim 1 and claim 7 have zero correlation (
ρ = 0). This suggests that if respondents like claim 1, it is uncertain if they will like claim 7.
- Claim 8 and claim 11 have high negative correlation (
ρ = -0.8). This suggests that respondents who like claim 8 tend to dislike claim 11.