Talk:Spearman's rank correlation coefficient

message: I was reading "Quantitative Measurement of Scores by Ranks" by Gayatri and Prasad and noticed substantial similarities in the text describing the Spearman's rank correlation coefficient and this wikipedia entry.  The text above is a conference paper from the "2011 International Conference on Advancements in Information Technology" and can be accessed HERE. (scroll down to the section on the Spearman CC for the text in question.
The following text from the Gayatri publication is a perfect match with the current wikipedia text:
"In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter ρ (rho) or as rs, is a non-parametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other."
The text in question dates from 2011, and the wiki article text is present in the history prior to this date.
216.15.21.56 (talk) 21:10, 9 January 2016 (UTC)
thread_title: Potential Copyright Violation:

message: It may be helpful to explain how the data is ranked eg from highest to lowest... and apparently Spearman's does assume the direction of relationship is constant eg rising or falling. -- —Preceding unsigned comment added by 82.37.10.168  (talk • contribs) 
* I agree, i think maybe having an example question would be very helpful and an explanation on the ranks as well. 82.198.250.66 08:20, 19 September 2006 (UTC)
thread_title: Suggestion

message: The difference between a one talied and two talied test isn't mentioned, not that I know what it is, neither is the need for a null hypothesis. Sam Hayes 09:20, 17 April 2006 (UTC)
thread_title:  One tailed, two tailed 

message: it would be helpful if this was explained easier since i am only a gcse student. i do not understand what is going on. :( -- —Preceding unsigned comment added by 86.7.149.45 (talk • contribs) 
:Yeah, it would help if it were made intelligible for those who can't understand maths. I don't understand the page and I've been doing this in college and uni. :'( —Preceding unsigned comment added by 86.1.198.198 (talk • contribs) 
:the reason you don't understand this is because the working is all wrong, the data has to be ranked from highest to lowest, not lowest to highest as has been shown. lauren campbell mon 26th february 2007. its not actually that difficult.
And the formula is (sometimes) incorrect. I know this and I'm only 14. Someone needs to do something about this, I would but I don't know how to do the symbols. George bennett 09:01, 9 July 2007 (UTC)
thread_title:  help 

message: The authors don't mention the number of degrees of freedom for the aforementioned t-distribution which makes the information useless in practical terms :-|
89.164.3.138 07:55, 22 December 2006 (UTC)
thread_title:  Hypothesis test with the student  t-distribution 

message: That walks through the process of doing spearman's rank by hand. I'm not sure if I got the right tone but hey. I just think this article was crying out for an example. The data is my own of course. --Grimboy 14:38, 10 February 2007 (UTC)
replies: text: Thanks!  The example was good but I've just changed it to remove all ties.  It seems the formula cannot be used for ties.  There is a tie-corrected formula and it seems the Japanese wikipedia page has it, but I cannot read it...  --Rayjapan 05:53, 29 May 2007 (UTC)

text: The value of "d" in the example is lacking the correct sign. For clarity, it should be |d| or the minus sign should be added where appropriate.  —Preceding unsigned comment added by 131.130.41.124 (talk) 13:39, 15 January 2008 (UTC)
thread_title:  I've just added an example 

message: The article states that Spearman's rho is a case of Spearman's rank correlation. Really? I think, these are two names for the same thing, and the formula named here "Spearman's rho" is just one of estimators used to estimate Spearman's rank correlation/Spearman's rho in population. Olaf m (talk) 00:49, 16 May 2008 (UTC)
replies: text: Fixed. Olaf m (talk) 19:11, 28 May 2008 (UTC)
thread_title:  Spearman's rho vs. Spearman's rank correlation 

message: Over on Judgment of Paris (wine) there's a statement "The original rankings appear to have been valid. The original and the Ashenfelter and Quandt re-calculations demonstrate a very high Spearman rank order correlation coefficient of .923." I have no idea whether this is 100% accurate or 100% BS. If anyone has a spare moment, could they have a look at this? Nunquam Dormio (talk) 15:46, 14 July 2008 (UTC)
thread_title:  If anyone has a spare moment… 

message: Pearson product-moment correlation coefficient, a similar correlation method that instead relies on the data being linearly correlated.
In what sense does it "rely" on the data being linearly correlated?  It's legitimate for any bivariate distribution. 72.75.93.12 (talk) 22:33, 9 April 2009 (UTC)
thread_title:  Pearson correlation 

message: I am concerned that the statement "without making any assumptions about the joint frequency distribution of the variables" may be somewhat misleading to readers. In particular that it would be read to imply that this is something special to Spearman's rank correlation coefficient; however other common measures of correlation, e.g. Kendall's tau and Pearson product-moment do not make any assumptions about the joint frequency distributions either; maybe rephrasing to something like "and whose properties are robust to assumptions about the joint frequency distribution of the variables"?
Aetheogamous (talk) 16:31, 21 April 2009 (UTC)
replies: text: The intent is to say that it does not prejudge the form of the relationship between variables in the sense that the Pearson coefficient "looks for" a linear relationship. It is this aspect of the joint distribution that is being targetted in what is written, not the marginal distributions and not the distribution of the residuals as might be implied by considerations of robustness. Of course, there may also be separate considerations of robustness, but I am not sure that you can do/say anything about aboutness unless you start making assumptions about the form of relationship. However, there may be something worth saying (if not already said) that the value of the coeeficient is invariant to montone transformations of either or both variables.  Melcombe (talk) 16:57, 21 April 2009 (UTC)
replies: text:  Since there are several aspects of the joint distribution at play here and one in particular is being targeted, maybe the text could directly target the aspect that is intended. Something like "without making any assumptions about the nature of the monotonicity"? Aetheogamous (talk) 17:27, 21 April 2009 (UTC)
replies: text: I have put in an attempt to do this, but the extra sentence added now needs further thought. Melcombe (talk) 08:59, 22 April 2009 (UTC)
thread_title:  joint frequency distribution of the variables 

message: I just found the following in this article:
: with degrees of freedom N-2.
I changed it to this:
: with degrees of freedom N − 2.
Even today, some people don't know the norms of WP:MOSMATH. Michael Hardy (talk) 20:42, 10 June 2009 (UTC)
thread_title:  MOSMATH 

message: The MS Excel formulas
{=CORREL(RANK(x,x),RANK(y,y))}
or (for Excel 2010):
{=CORREL(RANK.EQ(x;x);RANK.EQ(y;y))}
work fine when data set has no ties.
When data set has ties use:
{=CORREL(RANK(x;x)+(COUNTIF(x;x)-1)/2;RANK(y;y)+(COUNTIF(y;y)-1)/2)}
or (for Excel 2010)
{=CORREL(RANK.AVE(x;x);RANK.AVE(y;y))}
Reference: Spearman's rank correlation coefficient in Microsoft Excel (German)  — Preceding unsigned comment added by Christian.sch (talk • contribs) 11:57, 21 July 2011 (UTC)
replies: text: And what is this doing in an encyclopaedic article? I strongly believe this should be removed. Tomcrocker (talk) 12:50, 10 October 2011 (UTC)
replies: text: This is an article talk page.  I agree that that information almost certainly should not be in the article, per WP:NOT#HOWTO, but the request does not seem absurd.  — Arthur Rubin  (talk) 13:46, 10 October 2011 (UTC)

text: Sorry, I didn't see it in the article, at first.  I removed it there.  — Arthur Rubin  (talk) 13:48, 10 October 2011 (UTC)
thread_title:  Implementations of Spearman's rho 

message: In classical/frequentist statistics, hypothesis testing is about population distribution. Therefore, if one is interested in a given parameter θ (something such as the expectation distribution, or anything else, "parameter" being understood in a broad sense), the hypothesis under test should be about θ. Testing something about a sample characteristic such as the empirical correlation coefficient, or the empirical mean (average sample value) is meaningless.
Now, lets talk about the Spearman correlation coefficient and call it Γ. It is correctly defined in the article and it should be clear to everyone that its value is entirely known as soon as we observe the sample values of (X_i,Y_i), i=1,...,n. In other words, the Spearman coefficient cannot be a population parameter. As such, things such as "testing the value of the Spearman coefficient" or "testing whether it is zero" make no sense.
Taking things a bit further, one may think of the Spearman coefficient Γ as a random variable resulting from making n identical independent draws at random in the distribution of (X,Y). Now Γ has as distribution that depends on that of the pair (X,Y) and on the number n of pairs drawn to constitute the sample. This distribution certainly has an expectation and moments of any order (by construction, Γ is a bounded random variable). Now this distribution and its moments are a population characteristics. If one talks about testing something on Γ, the hypothesis under test should be about the (population) distribution of Γ (or its parameters).
Now my question : when the artcle talks about "testing if rho is 0", what is the null hypothesis ? In what aspect this null hypothesis does restrict the possible population distribution of (X,Y) ? Is the null hypothesis saying that X and Y are independent (P(X ∈ A, Y ∈ B) = P(X ∈ A) P(Y ∈ B)) ?  — Preceding unsigned comment added by 81.66.244.154 (talk) 17:57, 10 June 2015 (UTC)
thread_title:  Null hypothesis, what null hypothesis ? 

message: This article is in :Category:Information retrieval evaluation but skimming it I don't see how it's related to ranking of document retrieval results, like for Internet search engines.  If it is related, this should be explained in the article; otherwise, I think it should be removed from this category. -- Beland (talk) 16:54, 11 April 2016 (UTC)
replies: text: I suppose it could be used to compare attributes of different documents for similarity with each other or some sort of search vector. I found a paper describing this and another application, and added an "Applications" section. -- Beland (talk) 20:07, 17 January 2024 (UTC)
thread_title:  Related to document retrieval results ranking? 

message: The article contains the following text about pvrank:
pvrank is a very recent R package that computes rank correlations and their p-values with various options for tied ranks. It is possible to compute exact Spearman coefficient test p-values for .  Note: Package ‘pvrank’ was removed from the CRAN repository on 2017-04-24 "as check errors were not corrected despite reminders."  It is still available in the CRAN archive.
I would argue that we should remove the paragraph, given the (seemingly -- ass assesses by CRAN) lack of quality of the package.
Also, the package is not "very recent" at all. ~ Jotomicron 11:26, 27 July 2017 (UTC)
thread_title:  pvrank 

message: There's no way a lay reader would understand the definition given at the start. It might be technically accurate but it's in no way sufficient to explain what a Spearman rank correlation is.  — Preceding unsigned comment added by 82.12.149.135 (talk) 20:46, 23 October 2017 (UTC)
replies: text: Could you be more specific? Give examples of specific sentences or issues to improve upon? Tal Galili (talk) 09:03, 18 September 2021 (UTC)
thread_title:  Desperately needs simplifying