I'd like to estimate the correlation between:

An ordinal variable: subjects are asked to rate their preference for 6 types of fruit on a 1-5 scale (ranging from very disgusting to very tasty) On average subjects use only 3 points of the scale.

A continuous variable: the same subjects are asked to quickly identify these fruits, which results in an mean accuracy for the 6 fruits.

Is Spearman rho the best method to analyze these data and/or are there other good methods I could consider?

  • 1
    $\begingroup$ Welcome to the list. The ordinal variable looks like it is actually 6 variables (one for each fruit). It's also not clear to me how the identification variable is created, nor that it is continuous. $\endgroup$
    – Peter Flom
    Commented Dec 4, 2013 at 12:34
  • $\begingroup$ Thanks thats quick! So for each subject I indeed have 6 preference ratings, and 6 accuracy ratings. Accuracy is the mean hitrate over 16 identification trials (16 for each type of fruit). A hit is when they select the right fruit, miss is when they select the wrong type of fruit. Hope that this made it more clear. $\endgroup$
    – San
    Commented Dec 4, 2013 at 13:04

2 Answers 2


You could use Spearman's, which is based on ranks and therefore OK for ordinal data. You would then have six results.

If you want to take a different approach, you could get complex and look at a multilevel model, with subject being repeated. It sounds like "accuracy" would depend on "preference". So, a mixed model could look at that and account for the non-independence of the data. But, as noted, that's a much more complex model to implement.


According to this paper* "Measures of Association: How to Choose?" (doi:10.1177/8756479308317006), you should consider kendall's tau-b if the number of items in your ordinal variable is low (<5 or <6 ... this is a bit arbitrary).

If you have a large number of items in your ordinal variable, Spearman correlation would work well.

*the paper may be behind a paywall. But I tried to summarize the essence in my post.


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