How to detect 'secondary' correlations

As part of a Phase I SBIR study, we ran some experiments using human subjects. Without bothering with the specifics, we had a group of people complete a task using two different tools. The null hypothesis is that the time to complete would be independent of the tool used. Our study showed (using a paired T-test) that there is a statistically significantly difference in the time needed to complete the task based on the tool. This is good.

What I want to do now is to check to make sure that there are not other factors that could have accounted for this. For example, I want to be sure that the difference was not due to the subject's age, gender, etc.

My data looks something like this:

sub gend age tool time
001 1 27 1 32
001 1 27 2 20
002 2 34 1 33
002 2 24 2 19

I think that the proper method to use is a MANOVA, but, in all honesty, statistics is not my area of strength. I look forward to hearing your suggestions for helping me to better analyze these data.

Thank you - happy new year!