Use the Spearman rank correlation.is there association? if so how would i show it?
With kind regards
K.
Hey guys,
I've finished analyzing my data for an article regarding decompresion in divers.
My dependent variable is ordinal - 1-no recovery 2-partial 3-compelte recovery, and the independant variable is the time it took to treat the patients.
When i do an ordinal logistic regression - i get p=0.048.
However when i thought about showing the assosciation - i tried to divide the time to brackets such as 48-71 hours, 72-96 hours >96 hours and i've done CHi-square for the table of 3X3 - which turns out insignificant.
So what's the real thing? is there association? if so how would i show it?
thanks for your help
Amir
Use the Spearman rank correlation.is there association? if so how would i show it?
With kind regards
K.
Can u please explain?
Y correlation here instead.of.regression?
also - this will try to correlate an ordinal variable with a numeric continuous one. i thought you can't do it. Also dont' you lose the ordinal importance?
i ran it and came out non significiant.
Power might be the issue, why one test is showing signficant and another not. Generally you should not take an interval value like time and convert it into an ordinal one. You lose information (a concept I don't really understand, but I know is strongly frowned on by statisticians who do).
While spearman is commonly used with ordinal variables polychoric/tetronic is also useful. Some recommend it over spearman if you can make the, unknowable, assumption that there is a latent continuous variable behind the measured ordinal ones. Many theories of logistic regression make this assumption anyhow so it may make more sense to use this with your data.
http://en.wikipedia.org/wiki/Polychoric_correlation
One down side is that this is not simple to do in most software. SAS uses a special macro and SPSS (amusingly) uses R.
I have not heard that you can not use Spearman or polychoric correlations with one interval variable. You can certainly do Chi Square tests of independence with these (that is one of each).
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
Have you tested the power of the methods you are using? My guess is that each has different power in this case and that is why you are comming up with some signficant and some not.
I am not sure what you mean by not showing the data visually, but I would imagine there is some table or graphical approach that can show nearly anything in regression. Also purely descriptive results don't have to come from regression even if you are using regression to test the hypothesis.
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
I cannot assess whether the ordinal regression analysis met all
assumptions.
What you mean by non-significant, of course I do not know. Could be
p=0.050 or p=0.98.
Regarding usage and interpretation of Spearman's rank correlation,
there's tons of material in the net.
With kind regards
K.
Karabiner -
Spearman's p=0.707.
Still I though regression was the way to go inssead
Motesi- all assumptions are ok for my regression. I am not sure how i check the power for each test
and still - where lies the truth then?
about visualizing - i wanted to visualize the significant regression in graph...and figured i can't do it without bracketing the independent variable (time) to brackets...
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