Posted by
Sean McKenzie on
Sep 20, 2006; 9:57pm
URL: http://spssx-discussion.165.s1.nabble.com/Association-between-two-nominal-variables-tp1071026p1071036.html
Two Cents.
My background is mainly as a MacroEconomist forecasting time series. I have
particularistic rules of thumb, like in a single variable equation where I
see R2>0.5 I say its good, thise means an R of .71+, but four multivariate
equations you tend to be looking for much higher numbers.
If you are casually working in the social sciences, especially outside of
economics, the above or below as rules of thumb for the non expert are OK, I
would say, but yes it is all particularistic.
In dealing with my undergraduate students, interns et al, a classic is they
want to take the ratio of the variance to the mean, and I point out to them
that for a Standard Normal Variable that Statistic Can not be calculated;
OTOH >manufacturing tolerance or mtbf very often deal with situations where
a 0.98 is too weak and a product is< that measure is De Rigeur in other
situations.
Another one is contants of regression.
For those of you who read Barrons or Investor's Business daily, Alpha and
Beta in evaluating stocks is based on a simple regression like this:
Stock Price=Alpha + Beta*S&P500
And so Alpha is interpreted as some autonomous aspect of a stocks return,
and Beta its correlation with the overall market.
Simple Keynesian Consumption Functions (in economics) you will still see
explained in the same way:
Consumption=A+B*Income
Where A is interpreted as some autonomous component of consumption and B is
called the MPC=Marginal Propensity to Consume.
Stuff like this is still in many undergraduate text books, and talked about
by economists et al all the time.
The thing is these interpretations have been around for many years as the
Analysis goes back to the 19320's and 30's when running an equation per
semestre was a big deal.
When I was a first year graduate students asking for interpretations of
constants of regression was common, yet our professors would say that we
usually do not try to interpret them, even if we always include them. (even
though we still talks about Keynes and autonomous components of consumption,
anyone here an economist of more recent vintage than myself? Have they
finally stopped saying this stuff?).
The thing is that as you add and subtract variables from your equations
these constants move from positive to negative, from low to big magnitudes
etc..Do a classic Keynesian Consumption Function but then add in Interest
rates or exchange rates and see what happens.
Too new a user of SPSS to say anything useful, so I thought I'd throw in my
2 cents.
>From: Scott Czepiel <
[hidden email]>
>Reply-To: Scott Czepiel <
[hidden email]>
>To:
[hidden email]
>Subject: Re: Association between two nominal variables?
>Date: Wed, 20 Sep 2006 15:27:07 -0400
>
>Does anyone else experience pangs of nausea upon seeing this
>"interpretation" of the correlation coefficient? General rules of thumb
>like this are highly dangerous: what constitutes a weak or strong
>correlation is entirely dependent on context. For example, if you found a
>0.7 correlation between number of planes that take off and the number that
>subsequently land, would you really be happy to conclude that you'd found a
>strong relationship? Likewise, studies of manufacturing tolerance or mtbf
>very often deal with situations where a 0.98 is too weak and a product is
>scrapped or a factory floor is shut down. Please be highly skeptical of
>such reductionistic attempts of making statistics too easy!
>
>
>>.0 to .2 weak or no relationship
>> >.2 to .4 weak relationship
>> >.4 to .6 moderate relationship
>> >.6 to .8 strong relationship
>> >.8 to 1.0 very strong relationship
>> >(Salkind, Neil "Statistics for People who think they hate statistics,
>>2000
>> >pg. 96)
>>