Identifying confounders in multiple linear regression I am currently trying to identify confounders in a multiple linear regression, but I am a little unsure of a couple of steps. These are the steps I am taking:

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*Check to see if the potential confounders have a significant association with the dependent variable y.

*Check to see if the potential confounders have a significant association with the main independent variable x.

*If 1) and 2) are significant, check for a 10% change in beta coefficient.

However, when checking 1) and 2), do I put all of my potential confounders in the model, or do I check them one-by-one? So for example, y = x + gender + education + age, or y = x + gender and then y = x + education and then y = x + age ? My thinking was that when you check them one-by-one, there could be residual variables that are uncontrolled for, whereas if you put them all in one model, then you're looking more at the individual effects of the potential confounder on the dependent and independent variables.
Thanks for the help!

Data driven methods for identifying confounders are fraught with difficulties.  As an example, steps 1 and 2 depend on the power of the associated test, and introduce uncertainty into the analysis which can not be properly accounted for in the final inference.
The best approach to identifying confounders is to draw a directed acyclic graph (DAG) for your causal model and determine an identifiability strategy.  More on this can be found in most any causal inference text, but I recommend this book and this book.
Additionally, you should never check which variables to include/exclude from the model (for any reason) via a stepwise procedure as you describe.  See this answer for more.
