What statistics should i use? Pearson r or spearman rho My study is about the correlation of the ratings of the respondents to the product to their willingness to purchase it. I also made use of likert scale. My values are ordinal and i also have outliers after making use of scatter plot. But it still showed linearity.
 A: If you assume linearity behind your ordinal values (linearly assign values to the ranks), then Pearson and Spearman have identical coefficients!
Spearman is used to diminish the effect of outliers (which are not part of your assumed normal-distribution of the observable) by assigning ordinary values to them. Since your data is already in ordinary scale, it doesn't do anything other than Pearson.

edit: (an example in Python)
Imagine the flowing ordinal values were used to describe your product:
purchase_willingness = [low, rather_low, high, rather_high]
rating = [rather_bad, bad, rather_good, good]

And linearly assigned values by their intrinsic order (that all ordinal scales have):
   low, rather_low, rather_high, high = 11 ,13, 15, 17
   bad, rather_bad, rather_good, good = 31, 33, 35, 37

Then here is the result of Spearman vs Pearson:
scipy.stats.spearmanr(purchase_willingness, rating)
SpearmanrResult(correlation=0.6000000000000001, pvalue=0.3999999999999999)

scipy.stats.pearsonr(purchase_willingness, rating)
(0.6, 0.4)

As you can see, the result is identical.
Also note that it is enough to linearly assign values, you don't need to assign ranks or whatever was meant with the comment below.
