I have been reading a few papers lately that has done both bivariate and multivariate analysis on their data. What I have seen most of the times is that they usually do the bivariate analysis first, and if the p-value is below 0.05 they will do the multivariate as well. But what I have been reading that is, or at least CAN be an erroneous approach since there might not be much connection between variables if done alone, but sometimes when in combination with others, and reversely.
So what would be the correct approach ?
Let's say I want to make some kind of model that takes the important variables into account. What if the p-value is <0.05 for bivariate analysis, but above when multivariate is used. And what if it is above in bivariate and below in multivariate etc.
Basically, what would be the correct approach, or are there many different ?