Hi Miner,

Thank you for your prompt reply. Please allow me ask a couple of follow-up questions. I'm struggling to understand the concept as it differs from regular ANOVA and GLM; most of my work has been running participants in small behavioral studies for psychological evaluations, not manufacturing process improvement and optimization. So, I'm used to collecting data from a sample of participants where there are only 1 or 2 independent variables, and the other variables are covariates.

Normally, when comparing means (e.g between men and women), you calculate the mean for each group, which has a corresponding Std Dev; so the mean has variance within each cell. Thus, in each experimental condition you have multiple subjects contributing to the mean. However, in DOE, it seems that you have 1 subject for each condition (run) - so where does the variance come from? (you see how I'm looking at this?)

Here's the actual problem:

I have a product design element (e.g. a button) which has 5 parameters that could be adjusted in a binary manner (e.g. low / high). Each of these parameters is believed to affect the user's perception of the quality of the product / experience (in this case the quality is really really important as there are both ergonomic and manufacturing costs associated with these decisions).

We need to know which variables affect the user's perception of quality, how what the optimal level setting is for each parameter - seems like a DoE experiment? The only problem is that, as with all psychological studies, we don't sample 1 subject(participant) per condition (run?) - we sample a group of participants (e.g. N= 10) in order to get a more realistic estimate of the actual mean and to generalize to some population. So, perhaps I'm trying to fit a square peg in a round hole, but really, I'm not entirely sure how I should go about this at all.

Right, so my brain is stuck on t-tests, and simple repeated measures experiments, and at most MANOVA, but from a behavioral sciences perspective. I appreciate your assistance, and could benefit from a little hand-holding in this case.

Thank you very very much for you willingness to share your expertise and knowledge on this topic.

Ryan