As they are both non-normal distribution, linear correlation using Pearson is not a preferred option. Spearman is fine if the correlation relationship is fairly strong. Another method is to first transform the variable to normal distribution (for example, using log transformation), then using Pearson.

Probably Pearson but to be sure look at the conditions under which each coefficient is valid. From what you said it appears to be Pearson.

Best wishes, David Booth

I will go for Spearman’s Rank Order correlation analysis due to the non-normal distribution and when no transformation of the data can be found to approximate a bivariate normal distribution. You may consult for better comprehension 'TESTS FOR CORRELATION ON BIVARIATE NONNORMAL DISTRIBUTIONS' by Louanne Margaret Beversdorf using the following link:

https://digitalcommons.unf.edu/cgi/viewcontent.cgi?article=1293&context=etd

Could you try regression analysis? Plotting the data, plotting observed versus expected, and plotting the residuals may tell you much more about your data. It might help you decide on a good transformation, or that some other method is needed.

Just a comment but the question says nothing about confidence intervals or hypothesis tests so how are non-normal distributions relevant? Plus how do you calculate spearman on continuous variables?

Best wishes, David Booth

BTW I call your attention to the following link:

https://www.google.com/search?q=spearman+vs+pearson&ie=utf-8&oe=utf-8&client=firefox-b-1

Thank you so much for your answers!

Using Pearson or Spearman depends on the normality test.

Greetings!

The Pearson correlation coefficient is the most widely used. It measures the strength of the linear relationship between normally distributed variables. When the variables are not normally distributed or the relationship between the variables is not linear, it may be more appropriate to use the Spearman rank correlation method.

There is a very interesting paper about the differences between these two correlation coefficients on the same sets of data:

http://geoinfo.amu.edu.pl/qg/archives/2011/QG302_087-093.pdf

Hope it will be useful!

Hello

Difference between Spearman rank correlation coefficient and Pearson correlation coefficient :

1)Spearman correlation coefficient is used to investigate nonlinear relationships, while Pearson correlation coefficient is used to investigate a linear relationship.

2)Spearman correlation coefficient calculation is simpler and requires fewer assumptions than Pearson's coefficient

3)Pearson's correlation coefficient is used to calculate the correlation of two relative variables, but Spearman's correlation coefficient ,shows the correlation between the two ordinal variables.

If two variables follow a certain (normal) distribution, we use Pearson's coefficient to investigate the relationship, and if both are nonparametric, we will use Spearman's correlation coefficient, but the important point is that if one is parametric and the other is nonparametric These two methods are no longer applicable and we need to use regression to investigate the relationship between the two variables.

For non-normal distribution is Spearman rank correlation the method of choice.