I'm analysing the results to a satisfaction survey. I want to work out the key drivers of satisfaction so I'm using a question relating to overall satisfaction as my dependent variable and other satisfaction questions such as satisfaction with cost and product features as independent variables. In total I have about 11 independent variables (all with adequate sample sizes).

I run multiple linear regression in SPSS and remove independent variables from the regression until all remaining ones are significant (< 0.05) and I'm left with 3 variables, which from my understanding means these can be used to 'predict' overall satisfaction.

I have a strong suspicion that independent variables that were not significant during the regression are actually strong drivers of satisfaction. For example cost which was not significant, if customers were much less satisfied with cost (most would have been happy), then that would have a direct impact on overall satisfaction. Therefore I believe it is a driver of satisfaction and the regression is flawed?

I run bivariate correlation and all of the independent variables have fairly strong/strong correlations with the dependent variable and some of the strongest correlations are between independent variables that were not significant during the regression analysis and the dependent variable. I realise that this just means there is a linear relationship between the two.

However, I'm confused as to how I interpret these results and get to the bottom of what is driving satisfaction....

Help please?

Thanks!