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Well, he doesn't mean multiple correlation. He
simply means a bivariate Pearson corrlations between each subscale of EG and
JP and checking which correlation is higher.
We don't understand what advantage regression has over this approach.
Cheers
Humphrey
--- On Fri, 1/1/10, Hector Maletta <[hidden email]>
wrote:
From: Hector Maletta <[hidden email]>
Subject: Re: Regression or correlation
To: [hidden email]
Date: Friday, January 1, 2010, 11:09 AM
Multiple correlation is an implication of multiple regression.
There is a squared multiple correlation coefficient (R2) that arises as a
result of multiple regression. It can also be obtained by combining simple
correlation coefficients, obtaining partial correlation coefficients of
several orders, and finally get to R. But the question referred to SIMPLE
(BIVARIATE) correlation of each predictor with the outcome.
Hector
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Albert-Jan
Roskam
Sent: 01 January 2010 13:59
To: [hidden email]
Subject: Re: Regression or
correlation
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But
aren't multiple correlation and multivariate regression basically the same
thing?
I would prefer multiple regression because one has an idea of the
individual amounts of explained variance (contrary to the R2), and also
because the CI95% is easily obtained.
Cheers!!
Albert-Jan
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the face of ambiguity, refuse the temptation to guess.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--- On Fri, 1/1/10, Hector Maletta <[hidden email]>
wrote:
From: Hector Maletta <[hidden email]>
Subject: Re: [SPSSX-L] Regression or
correlation
To: [hidden email]
Date: Friday, January 1, 2010, 2:51 PM
Your friend is plain wrong. The different
subscales are correlated among them, and each probably predicts only part
of the variance in job success. Using the various subscales will cover more
variance, may predict better, and may also reveal the different weight of
the subscales in explaining JB. The subscale with the highest bivariate
correlation may ultimately have a lower regression coefficient.
Your friend could be right if only one subscale
has a high correlation and all the others have not, but that is not likely
to be the case.
Finally, remember than prediction is a
probabilistic affair. You cannot predict INDIVIDUAL job success: what
correlation or regression may tell you is the EXPECTED job success of a
GROUP of people sharing the same value of predictors. Likewise, smoking is
a predictor of lung cancer, but you cannot predict lung cancer for specific
individuals: some smokers live to their 90s, and some non-smokers get lung
cancer anyway. You can only predict that the RELATIVE FREQUENCY of lung
cancer would be much higher among smokers than non-smokers (assuming all
are representative of the general population: the prediction may break down
if a majority of non-smokers, and a minority of smokers, happen to be coal
miners or some such, which makes them more likely to breath in toxic
substances causing lung cancer). In the latter case (coal mining as a
secondary predictor) including occupation in the regression equation would
help separate the two as independent causes of lung cancer..
Hector
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Humphrey Paulie
Sent: 01 January 2010 05:59
To: [hidden email]..EDU
Subject: Regression or correlation
|
Dear
folks,
I
am studying the relationship between EQ and job success (JB). I also want
to know which of the subscales of EQ is a better predictor of JB.
I
think I should use multiple regression.
My
colleague, however, says there is no need to take the trouble and
complexities of regression. We can simply correlate each subscale of EQ
with JB separately and compare the correlations. He believes that the
subscale that has the highest correlation with JB will also turn out to
be its best predictor in regression analysis. And that regression is an
unnecessary and useless statistical development. There is nothing
regression does that correlation cannot do!!! I cant think of anything to
justify the superiority of regression over correlation here.
Can
you please help me justify it?
Cheers
Humphrey
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