To conduct an
experiment with K categorical factors and one continuous factor, 2-level factorial
designs are common. To create a 2-level factorial design, select Estimate main and
interaction effects when all factors have two
levels.
Consider whether you need a design for a more specific case. If you have a
hard-to-change factor, select Estimate main
and interaction effects when all factors have two levels, and one is
hard to change or Estimate main and interaction effects when some factors are hard to
change. The visible option depends on the number of factors to study.
Decision details
The following information defines main effects, interaction effects, and a
hard-to-change factor.
- What is a main effect?
- A main effect is an estimate of the effect of a single factor. For
example, fertilizer company B is comparing the plant growth rate
measured in plants treated with their product compared to plants treated
by company A's fertilizer. In the experiment, fertilizer B has a higher
plant growth rate mean than fertilizer A. The difference in the means is
the main effect of the fertilizer factor.
- What is an interaction effect?
- An interaction effect is an estimate of the way that the effect of one
factor depends on the value of one or more other factors. For example,
if the levels are wide enough, the effect of time on the quality of a
baked product depends on temperature. When the temperature is so low
that the product is under cooked, then an increase in time increases the
quality. When the temperature is in an acceptable range, an increase in
time decreases the quality because the product burns. The effect of time
depends on the value of temperature.
- What is a hard-to-change factor?
- A hard-to-change factor is a factor that is difficult to randomize
completely because of time or cost constraints. For example, temperature
is a common hard-to-change factor because adjusting temperature often
requires significant time to stabilize. A split-plot design is a
designed experiment that includes at least one hard-to-change factor. In
a split-plot experiment, levels of the hard-to-change factor are held
constant for several experimental runs.
