I am looking at survey data that measures satisfaction with provided services . I am interested in exploring the relationship between the time the product is used (measured in days, ranging from 10 to 3000) and satisfaction.
- ~ 1k responses
- "Satisfaction" has two peaks around 2 and 7
- "Time used" is peaking around 800 ( long right tail)
- Due to the nature of the product, there is no reason to assume that users, who use it for a long time are satisfied with it( = There is no reason to expect that if a user unsatisfied (s)he stops using it)
- What would be an appropriate metric to measure the degree of association between two variables?
- What could "go wrong"? ( what should I check before using a metric, what are the assumptions that the metric implies)
It looks like Pearson's and Spearman's correlations are both good candidates to start with, although there are several things that concern me
- Is that an issue that one of the variables is discrete and the other one is (sort of) continuous? (looks like it is not ?)
- Is that okay to have a bimodal distribution (satisfaction)?
- I keep seeing somesources saying that pearson's correlation assumes normal distribution, while other sources saying it is okay to have non-normal distributions. What is the source of this confusion? what am I missing?
I would appreciate any comments/suggestions. Thanks.