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As I am testing a number of models, I notice that none of my demographic variables are ever signficant. For example, I am testing a model to predict the dependent variable 'perceived substitutability'. The independent variables are contentment about the content on traditional television and contentment about the content on online television. I run this model together with gender, age, professional status, income (yes/no), family composition and marital status. I used a hierarchical method where I used the demographic variables in the first block and the other variables in the second block. As I run the regression, all demographic variables were not significant (p>0,05). This is of course due to correlations between the independent variables.

If I use one demographic variable at a time to explain the dependent variable (so six times a simple regression where no other demographic variables are used), professional status and income become significant in the simple regression (not together because of course they correlate). Can I use this in my result-section: that in the simple regressions (1 dependent, 1 independent) they are significant while in the multiple they were not?

I am using SPSS

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    $\begingroup$ Then I would report the regression results from the two contentments as a model apart from the two demographic variables that were significant. (in a simple regression and not together) because if I use the contentments together with the demographics, the demographic variables become non-significant $\endgroup$ – user45825 May 21 '14 at 14:32
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You can certainly report both simple and multiple regressions in a paper; I see this done all the time. You can then explain the differences, just as you have done above - but instead of saying things like "of course" I would, in a paper, show that they are related.

I was a bit puzzled by income being (yes/no) but that may just be due to an odd choice of variable name.

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  • $\begingroup$ Ok thank you for your answer! Is this a good justification that I did simple regressions because of the high correlations between predictors? Because that is in fact the reason. When I put all of them together in a multiple regression they are not significant and therefore I cannot discuss demographics. I must add that the correlations are between ,200 and ,500 but still I assume this is the reason that together they are not significant while apart they are $\endgroup$ – user45825 May 21 '14 at 16:04
  • $\begingroup$ Yeah, although you could also try collinearity diagnostics with condition indexes $\endgroup$ – Peter Flom - Reinstate Monica May 21 '14 at 19:17

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