Testing for correlation with count data I am attempting to test the correlation between two variables:
Predictor: Count data (not ranked)
Response: Continuous
Because my predictor variable is not continuous, I cannot use Pearson's, correct?
However, because it is also not rank data, I cannot use Spearman's, yes?
What test for correlation would you guys suggest?
edit: Response to @whuber:
Perhaps I am misunderstanding "rank".
My predictor variable is the size of a group and my response variable is essentially the score that a group receives on a test.
I am testing to see if there is a correlation between group size and score.
My thought is that because a group size of n-1 isn't necessarily "better" than a group size of n (like how 1st place is better than 2nd), the data is not "ranked".
 A: You misunderstand Spearman's rank correlation coefficient: the method is not for measuring linear association on rank data (per se), but a method for measuring monotonic association (ordinal association) of data (of whatever distribution) by using the ranks of those data.
To calculate Spearman's $r_{S}$ you simply: 


*

*rank each of your variables separately

*calculate Pearson's $r$ using those ranks, rather than the unranked data.


So Spearman's $r_{S}$ is fine for your purposes. (As is Kendall's $\tau$, and the general correlation coefficient.)
A: There are two ways I would approach this problem.
Depending on how many members your groups have, you could in fact consider group size to be a continuous variable. In that case, assuming that both 'score' and 'group size' are normally distributed and you have enough cases (depends on who you ask, some suggest at least 30, but it is contentious), you could run a Pearson's and/or a Spearman's correlation test.
On the other hand, if for whichever reason your 'group size' variable is not normally distributed (and assuming that 'score' is, and you have enough observations), you could also run a linear model (an ordinary least squares linear regression) with 'score' as your dependent variable and 'group size' as your independent variable (i.e. your predictor). 
