2
$\begingroup$

Is there a rule for selecting which experimental design one should carry out? I am new to statistics/ED and sometimes I find myself asking which design is best to use. My general guidelines have been (please correct if wrong):

  • Use a Plackett Burman (PB) for screening the number of variables
  • Use a fractional factorial if you have a large number of variables and cost/money is a factor as well as estimating effects
  • Use an orthogonal design if your variables have different number of levels
  • Use a full factorial if you do not have a large number of variables

Another sort of guideline I have is from here. I would like to have some input from members on their approach on this and if they do things differently.

EDIT: As per @Scortchi advice. Assuming you have a study which has 12 variables and another which 6 variables both with 3 levels for each variable. What type of design would you choose for each and why? The way i would approach this is by maybe screening the variables first as a full factorial would be too many cases to run then apply a full or fractional factorial. However I would then ask well why not do a orthogonal straight away. From the link i linked to earlier the objective of the design is a consideration so in the context of obtaining an optimal fit if you have +5 you would screen and then run a design, but why not run an orthogonal straight away. Hopefully this may be a bit clearer (apologies if the terminology I am using isn't correct)

$\endgroup$
1
  • 3
    $\begingroup$ Welcome to Cross Validated! This is a rather broad question - whole books have been written to answer it (see stats.stackexchange.com/questions/1815/… for some of them). Perhaps you could give a specific example, explaining what designs you're considering & what you're unsure about. $\endgroup$ – Scortchi - Reinstate Monica Jun 27 '17 at 13:31
1
$\begingroup$

Your approach may be good as a start, until you get more experience, at least for your application area (industry/processes?). But it is impossible to reduce experimental design to such a set of rules, especially if you want them to cover all application areas! So you should strive for an understanding where you base design on a few important principles, such as

  1. replication

  2. blocking

  3. factorial design/fractional factorials

  4. orthogonality

  5. confounding

then you will be able to construct a design for your needs. Then find a good book from the many mentioned here: Recommended books on experiment design?

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.