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]
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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