Re: Regression or correlation

Posted by Hector Maletta on Jan 01, 2010; 5:09pm
URL: http://spssx-discussion.165.s1.nabble.com/cronbach-alpha-for-binary-responses-tp1086058p1086064.html

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

 

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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

 

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] 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