Contents
What are marginal means in factorial design?
The first information is Marginal Means: Marginal Means are the means for one level of an independent variable averaged across all level of the other IV. Thus you have a Marginal Mean with A=1, which is the mean for everyone who experienced A at level one, regardless of whether they experienced B at 1 or 2.
What is contrast in factorial experiment?
Contrast analysis is helpful because it can be used to test specific questions of central interest in studies with factorial designs. It weighs several means and combines them into one or two sets that can be tested with t tests. The effect size produced by a contrast analysis is simply the difference between means.
How are the levels of a factorial design determined?
For those of you familiar with chemical or laboratory processes, it would not be hard to come up with a long list of factors that would affect your experiment. In this context we need to decide which factors are important. In these designs we will refer to the levels as high and low, +1 and -1, to denote the high and the low level of each factor.
How are two way interaction effects defined in factorial design?
Therefore all the two-way and three-way interaction effects are defined by these contrasts. The product of any two gives you the other contrast in that matrix. From these contrasts we can define the effect of A, B, and C, using these coefficients. The general form of an effect for k factors is:
What does the 2 k factorial design mean?
The 2 k refers to designs with k factors where each factor has just two levels. These designs are created to explore a large number of factors, with each factor having the minimal number of levels, just two.
How many possible conditions are in a factorial experiment?
Notice that the number of possible conditions is the product of the numbers of levels. A 2 × 2 factorial design has four conditions, a 3 × 2 factorial design has six conditions, a 4 × 5 factorial design would have 20 conditions, and so on. Also notice that each number in the notation represents one factor, one independent variable.