Significant correlate but not predictor

[Shilpa Bandyopadhyay]

Dear Neeraj Sir and all members,

I have done a study to understand the correlates and predictors of psychological well-being. Some variables that emerged as significant correlates of psy. well-being have not emerged as its significant predictors. For e.g., life satisfaction emerged as a significant correlate but not as a predictor. What does this imply? Is such a result possible?

I have done multiple regression analysis using the enter method. 

There is no issue of multicollinearity [VIF, tolerance] and auto-correlation [Durbin watson] in my data. I have checked for it. There are no outliers either. 

Thanking everyone in advance. 

__________

Warm regards, 

 Shilpa 

[Neeraj Kaushik]

Dear Shilpa

Correlation is not causation (Cause and effect relation).

It is possible that the 2 vars are correlated but technically not related.

I always cite the example that in one survey weight of the participants were found to be highly correlated with the IQ of the participants. Now, this makes no sense. Neither weight is a predictor of IQ nor IQ is a predictor of weight.

Here the participants were the young babies and with the increase in age of the babies, both weight and their IQ increased. So, both weight and IQ have a very high correlation but both of them depends on age (It shd be taken as the control variable).

That's why conceptualization of model (interplay of vars) is much more important than statistical tool/techniques. 

Furthermore, correlation provides us only a linear estimation of the 2 vars.

Ex: If we find the correlation between age and height among grown-up people (age >21 yrs), the correlation will be very small or zero as age is increasing daily but height is not increasing. So, age and height has a relation which is explained by log. But r determines their linear relation, hence there is so small or zero value.

Hope this helps.

If you've any doubts/observations, plz share.

 

Best wishes

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[Shilpa Bandyopadhyay]

Right, Sir. I completely understand your point. Thank you so much for such a detailed explanation. 

__________

Warm regards, 

 Shilpa 

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