Date:         Mon, 9 Jan 2006 15:31:14 -0800
Reply-To:     machelle <machellewilchesky@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         machelle <machellewilchesky@GMAIL.COM>
Organization: http://groups.google.com
Subject:      Re: New to Correlation
Comments: To: sas-l@uga.edu
In-Reply-To:  <1136516590.137596.231210@g49g2000cwa.googlegroups.com>
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Hi there
This is more of a stats question than a SAS question, and as others
have indicated to you, you can't correlate the type of variables you
are trying to correlate.

To find the association between an ordinal (age group) and a nominal
(mapped value)variable the correct test would be the Krusal-Wallis
test. This test is also used when looking at a nominal variable with an
interval or ratio variable if it's markedly non-normal.

In a nutshell, the decision you make regarding which type of bi-variate
analyses you perform is a function of the type of variables you have:

1) It they are both Interval/ratio variables, you can use proc corr and
check the pearson correlation coefficient;

2) If they are both ordinal,  or if they are both or one is
interval/ratio but markedly non-normal, you'd move to the Spearman
correlation coefficient;

3) If they are both nominal (i.e. they are categorical  without any
ordering involved, like colour or ethnicity) then you'd use a
Chi-square test of independence;

4) if one is dichotomous (falls into one of 2 values like 'yes' 'no')
and one is interval/ratio then you'd use a t-test;

5) if one is nominal with more than 2 categories, and the other was
interval/ratio, you'd use ANOVA

and finally,

6) For an Ordinal variable with a Nominal variable, you'd use
Kruskal-Wallis. As I mentioned above, this test is also used when
looking at a nominal variable with an interval or ratio variable if the
latter is markedly non-normal.


IF you wanted to start looking at associations and do modeling, then
you'd be looking at doing an ordinal logistic regression where:

your ordinal variable (age group) is your dependent variable
mapped_value would be an independent variable
and you could add other variables here to your model to check for
confounding, interaction, and make an attempt to get a precise estimate
of your effect.

I know this probably all sounds a bit daunting, but you asked to be
pointed in the right direction, and as someone else hinted, perhapse
you might find it helpful to get some statistical analyst support? Grad
students are usually good at things like this, and will usually be
happy to earn some extra money to help them during their studies.

I hope this helped...

Machelle