Quantitative or Qualitative
Both types of design are suitable for quantitative and qualitative factors alike.Screening designs or Plackett-Burman designs
Screening designs are particularly useful, if the experiment is intended to pick a few
important factors out of a list of many candidate factors. It often can not be ruled out
that factors interact with each other, but the interactions are not of interest at the
screening stage. If the main effects are stronger than the interactions, which is very often
the case, the screening experiment has a good chance of selecting the key factors
for further experimentation. With very high expertise and some luck, a screening
experiment might even be sufficient to draw further conclusions.Regular (fractional) factorial designs
Regular Fractional Factorial designs of resolution III are also sometimes used for
screening. If the aliasing is not too severe, if e.g. just three main effects are aliased with
one 2-factor interaction each (i.e. one word of length three), this can be reasonable. However,
with more severe aliasing, using a resolution III design is more risky than using a screening design,
because main effects are completely aliased with 2-factor interactions, which can lead to severe bias and
wrong conclusions.
Regular (Fractional) Factorial designs of resolution IV or higher are particularly
useful, if not only main effects but also 2-factor interactions are expected to be
active. Note that interactions between more than two factors are usually considered negligible.
This is usually appropriate, if the experimental region is not too large, since many
functions can be approximated well by first- or second-order polynomials over small regions.
If package FrF2.catlg128
is installed (this will not be necessary for standard use),
there is an Expert menu entry for loading catalogues from this package
for usage with regular fractional factorial 2-level designs.
On using this menu entry, the package is loaded, and a list of catalogues is shown.
Do not unnecessarily load them, because some of them are very large.Warning
Important: For both types of design, like also for other factorial designs,
the experiment must conduct all experimental runs as determined in the design,
because the design properties will deteriorate in ways not easily foreseeable,
if some combinations are omitted.
It must be carefully considered in the planning phase, whether it is
possible to conduct all experimental runs, or whether there might be restrictions
that do not permit all combinations to be run (e.g.: three factors, each with levels
small and large, where the combination with all three factors
at level large is not doable because of space restrictions).
If such restrictions are encountered, the design should be devised in a different way from the beginning.
If possible, reasonable adjustments to levels should ensure that a factorial design
becomes feasible again. Alternatively, a non-orthogonal D-optimal design can take
the restrictions into account. Unfortunately, this functionality is not yet implemented in this GUI.