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How should I analysis the correlation between four ordinal numbers (0,1,2,3) and various range of the continuous values? The scatter plot looks like a 4 parallel horizontal dots .

user27379
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  • Please show example data, and preferably code – Ben Allison Feb 20 '14 at 16:23
  • I would like to upload the plot of data but I am not able because of low reputation, can I have your email? – user27379 Feb 21 '14 at 10:21
  • Can you show the raw data table? That way someone else might be able to answer too. – Ben Allison Feb 21 '14 at 11:14
  • data1(0.045812) data2(.00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 1.00 .00 1.00 .00 .00 .00 .00 .00 1.00 .00 2.00 2.00 .00 1.00 .00 2.00 .00 .00 .00 .00 1.00 .00 .00 .00 1.00 .00 2.00 2.00 .00 .00 2.00 .00 1.00 1.00 2.00 2.00 2.00 1.00 2.00 2.00 .00 .00 .00 1.00 .00 1.00 2.00 .00 1.00 .00 1.00 .00 1.00 2.00 1.00 1.00 3.00 1.00 1.00 3.00 3.00 2.00 1.00 1.00 1.00 .00 2.00 1.00 .00 2.00 .00 .00 2.00 .00 .00 1.00 .00 2.00 .00 .00 .00 .00 1.00 2.00 2.00 2.00 .00 1.00 1.00 2.00 2.00 – user27379 Mar 05 '14 at 17:36

1 Answers1

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You could run a Spearman rank correlation test. Using R,

require(pspearman)
x <- c(rep("a", 5), rep("b", 5), rep("c", 5), rep("d", 5))
x <- factor(x, levels=c("a", "b", "c", "d"), ordered=T)
y <- 1:20

spearman.test(x, y)

    Spearman's rank correlation rho

data:  x and y
S = 40.6203, p-value = 6.566e-06
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9694584 

Warning message:
In spearman.test(x, y) : Cannot compute exact p-values with ties

Non-significant correlation

set.seed(123)
y2 <- rnorm(20)
spearman.test(x, y2)

Spearman's rank correlation rho

data:  x and y2
S = 1144.329, p-value = 0.5558
alternative hypothesis: true rho is not equal to 0
sample estimates:
     rho 
0.139602 

Warning message:
In spearman.test(x, y2) : Cannot compute exact p-values with ties
Whitebeard
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