I'm working on a project and was wondering how I could perform statistical tests between two correlations to prove significance.
The background of my project is that I was trying to see if Twitter sentiments influence a certain market more than the other. After collecting data and calculating the correlation values using three methods (Pearson, Kendall, and Spearman) I found that the difference is very small. However, I feel that simply saying "the difference is small" is not convincing and was wondering if there were specific statistical tests for this purpose.
I've done searching and have seen that people use p-value tests quite often, but I'm not sure if that would be appropriate for my case, as I'm also not familiar with statistical testing methods. I was hoping if anyone more knowledgeable would be able to give me some tips or pointers.
The data that I'm specifically using looks like this:
To briefly explain the data, TextBlob and NLTK Vader are two sentiment analysis tools I used. Within these two methods, I applied time lag to the data (one day forward and one day backward) and you can also see that I varied calculating correlation.
Y are two separate markets and the correlation is between
sentiment. The difference is the difference between these two.
My original hypothesis was that market
Y would have higher correlation, but apparently that is not true judging by the difference.
What might be some ways that I could formally prove the significance of my finding?