I have an existing data set that needs to be analyzed, but the existing set is comprised of a 2-level matrix and a set of three "centerpoints". I'd like to take advantage of the reproducibility information from the three centerpoints, but just throwing them in with the experimental matrix unbalances the analysis (there are a couple categorical factors in the original matrix).
Is there a valid way to do this?
Although unbalanced designs are not ideal, they can still be analyzed. Just put the data with the experimental results and analyze.
Other possible options:
Ignore the replicates, reducing the sample size. But sample size is actually more important than balance. So this is not a good option.
Perform additional experiments to get a balanced design. This route makes sense, if it is possible and the process has not changed from the first set of trials to the new set. You can add the additional runs by using the Augment Design option in JMP.
My comments. There isn't enough information on your experiment provided, but here are some thoughts. How can you have center points with categorical factors? AFAIK, center points require all factors be continuous and for the test for curvature the all quadratic terms are confounded. (Note: I have seen folks create surrogate center points with a couple of categorical factors, but that usually is just setting the categorical factors to one of the 2 levels). You can use the replicates of the center points as an estimate of the mean square error. In fact, if center point is the current conditions, replicates of this as an estimate of current process variance is an interesting test of significance (ANOVA) for the other factor effects.
@statmanGenerally we think of center points as being relevant to only continuous factors as you point out. But one can create a design which has continuous AND categorical factors AND center points. The usual convention, if you have the budget and resources, is to add treatment combinations to the experiment such that each center point combination of continuous factors is run at least twice for EACH level of the continuous factors. This arrangement maintains balance and estimation of all possible effects if running say a full factorial design...but as one finds out pretty quickly, the size of the design balloons pretty quickly.
A more prudent, economical and efficient approach would almost always be take advantage of optimal design techniques and build out the design to fit the problem vs. the method I cite above....which basically shoe horns the problem into the design because, 'well it's in the Classic catalog as the only way to do it'.
There are no labels assigned to this post.