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I have 3 variables. My DV $Z$ of interest are reaction times, IV $A$ and IV $B$ are performance scores in psychometric tests. Looking at bivariate correlations, $A$ and $B$ are negatively correlated (-.40), $A$ and $Z$ are marginally negatively correlated (-.10) and $B$ and $Z$ are highly negatively correlated (-.50). Putting $A$ and $B$ in a regression model with criterion $Z$ leads to two negative standardized coefficients about -.40. In other words, If I am good in $A$, I have a fast reaction. If I am good in $B$, I have a fast reaction. But if I am good in $A$, I am not good in $B$?! Is this logical?

Coding of the variables should be alright.

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Maybe the answer lies in the fact that some of the correlations are simply not significant in the sense that a large correlation factor might arise from a chance alignment of a small number of points. Looking at the different correlation coefficients, I would advance that maybe the correlation between Z and A is not that significant in this sense. A way of testing for this this is to do a permutation test (as a simple non-parametric test) which would associate a p-value to the correlation coefficient.

So in a nutshell, given the information you provided, the results you obtained are not fully incompatible.

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  • $\begingroup$ the bivariate correlation is not significant, nor the predictor. still, a standardized beta of -.36 is hard to ignore. i will do what you told me $\endgroup$ Commented Jun 10, 2014 at 16:09
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    $\begingroup$ @pedrofigueira: Could you explain in what way 'significance' is relevant here? To the OP: could you please provide some graphical description of the situation? This is much easier than to speculate using correlations. $\endgroup$
    – Michael M
    Commented Jun 10, 2014 at 17:57
  • $\begingroup$ @Michael Mayer I just did. Hope it helps. $\endgroup$ Commented Jun 10, 2014 at 21:55

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