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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
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In the face of ambiguity, refuse the temptation to guess.
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--- 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
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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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