- Define main effect, simple effect, interaction, and marginal mean
- State the relationship between simple effects and interaction
- Compute the source of variation and df for each effect in a factorial
design
- Plot the means for an interaction
- Define three-way interaction
Basic Concepts and Terms

In the Bias
Against Associates of the Obese case
study, the researcher was interested in whether the weight of
a companion of a job applicant would affect judgments of a male
applicant's qualifications for a job. Two independent
variables were investigated: (1) whether the companion was obese or of
typical weight and (2) whether the companion was a girl friend
or just an acquaintance. One approach would have been to conduct
two separate studies, one with each independent variable. However,
it is more efficient to conduct one study that includes both independent
variables. Moreover, there is a much bigger advantage than efficiency
for including two variables in the same study: it allows a test
of the interaction between
the variables. There is an interaction when the effect of one
variable differs depending on the level of a second variable. For example, it is possible that
the effect of having an obese companion would differ depending
on the relationship to the companion. Perhaps there is more
prejudice against a person with an obese companion if the companion
is a girl friend than if she is just an acquaintance. If so,
there would be an interaction between the obesity factor and
the relationship factor.

There are three effects of interest in this
experiment:

- Weight: Are applicants judged differently depending on the
weight of their companion?

- Relationship: Are applicants judged differently depending
on their relationship with their companion?

- Weight x Relationship Interaction: Does the effect of weight
differ depending on the relationship with the companion?

The first two effects (Weight and Relationship) are both main
effects. A main effect of an independent variable is the
effect of the variable averaging over the levels of the other variable(s).
It is convenient to talk about main effects in terms of marginal
means. A marginal mean for a level of a variable is the
mean of the means of all levels of the other variable. For example,
the marginal mean for the level "Obese" is the average
of "Girl-Friend-Obese"
and "Acquaintance-Obese." Table 1 shows
that this marginal mean is equal to the average of 5.65 and 6.15
which is 5.90. Similarly, the marginal mean for Typical is the
average of 6.19 and 6.59 which is 6.39. The main effect of Weight
is based on a comparison of the these two marginal means. Similarly,
the marginal means for Girl Friend and Acquaintance are 5.92 and
6.37.

In contrast to a main effect which is the
effect of a variable averaged across levels of another variable,
the
simple effect of a variable is the effect
of the variable at a single level of another variable. The simple
effect of "Weight" at the level of "Girl Friend" is
the difference between the "Girl-Friend
Typical"
and the "Girl-Friend Obese" conditions. The
difference is 6.19-5.65 = 0.54. Similarly, the simple effect
of "Weight" at the level of "Acquaintance" is
the difference
between the "Acquaintance Typical" and the "Acquaintance
Obese" conditions. The difference is 6.59-6.15 = 0.44.

Recall that there is an interaction when the effect
of one variable differs depending on the level of another variable.
This is equivalent to saying that there
is an interaction when the simple effects differ. In this
example, the simple effects are 0.54 and 0.44. As shown below,
these simple effects are not significantly different.

Tests of Significance

The important questions are not whether there
are main effects and interactions in the sample data. Instead,
what is important is what the sample data allow you to conclude
about the population. This is where Analysis of Variance comes
it. ANOVA tests main effects and interactions for significance.
An ANOVA Summary Table for these data is shown in Table 2.

Consider first the effect of "Weight." The degrees
of freedom (df) for "Weight" is 1. The degrees
of freedom for a main effect is always equal to the number of
levels of the variable minus one. Since there are two levels of
the "Weight"
variable (typical and obese) the df is 2 -1 = 1. We skip the calculation
of the sum of squares (SSQ) not because it is difficult, but because
it is so much easier to rely on computer programs to compute it.
The mean square (MS) is the sum of squares divided by the df.
The F ratio is computed by dividing the MS for the effect by the
MS for error (MSE). For the effect of "Weight," F =
10.4673/1.6844 = 6.214. The last column, p, is is the probability
of getting an F of 6.214 or larger given that there is no effect
of weight in the population. The p value is 0.0136 which is quite
low and therefore the null hypothesis of no
main effect of "Weight" is rejected.
The conclusion is that the weight of the companion lowers judgments
of qualifications.

The effect "Relation" is interpreted the same way.
The conclusion is that being accompanied by a girl friend leads
to lower ratings than being accompanied by an acquaintance.

The df for an interaction is the product of the
df of variables in the interaction. For the Weight x Relation
interaction (W x R), the df = 1 since both Weight and Relation
have one df: 1 x 1 = 1. The p value for the interaction is 0.8043
which is the probability of getting an interaction
as big or bigger than the one obtained in the experiment if there
were no interaction in the population. Therefore, these data provide
no evidence for an interaction. Always keep in mind that the lack
of evidence for an effect does not justify the conclusion that
there is no effect. In other words, you do not accept the null
hypothesis just because you do not reject it.

For "Error," the degrees of freedom is
equal to the total number of observations minus the total number
of groups. The sample sizes for this experiment are shown in Table
3. The total number of observations is 40 + 42 + 40 + 54 = 176.
Since there are four groups, df = 176 - 4 = 172.

The final row is "Total." The degrees
of freedom total is equal to the sum of all degrees of freedom.
It is also equal to the number of observations minus 1, or 176
-1 = 175. When there are equal sample sizes, the sum of squares
total will equal the sum all other sums of squares. However, when
there are unequal sample sizes, as there are here, this will not
generally be true. The reasons for this are complex and are discussed
in the section Unequal n .

Plotting Means

Although the plot shown in Figure 1 illustrates
the main effects and the interaction (or lack of interaction)
clearly, it is called an interaction
plot. It is important to carefully consider the components of this plot.
First, the dependent variable is on the Y axis. Second, one of the independent
variables is on the X axis. In this case, it is the variable is "Weight." Finally,
a separate line is drawn for each level of the other independent variable. It
is better to label the lines right on the graph as shown here than with a legend.

If you have three or more levels on the X axis, you should not
use lines unless there is some numeric ordering to the levels.
If your variable on the X axis is a qualitative variable, you
can use a plot such as the one in Figure 2. However, as discussed
in the section on bar charts, it would be better to replace each
bar with a box plot.

Figure 3 shows such a plot. Notice how it contains information
about the medians, quantiles, and minimums and maximums not
contained in Figure 2. Most important, you get an idea about how
much the distributions overlap from Figure 3 that you do not get
from Figure 2.

Line graphs are a common option with more than two levels
if the variable is numeric. Figure 4 shows an example. A line
graph has the advantage of showing the pattern of interaction
clearly. Its disadvantage is that it does not convey the distributional
information contained in box plots.

An Example with Interaction

The following example was presented in the section
on specific comparisons among means. It is also relevant here.

This example uses the made-up data from a hypothetical
experiment shown in Table 4. Twelve subjects were selected from
a population of high-self-esteem subjects and an
additional 12 subjects were selected from a population of low-self-esteem
subjects. Subjects then performed on a task and (independent
of how well they really did) half were told they succeeded and
the other half were told they failed . Therefore
there were six subjects in each esteem/success combination and
24 subjects altogether.

After the task, subjects were asked to rate (on
a 10-point scale) how much of their outcome (success or failure)
they attributed to themselves as opposed to being due to the nature
of the task.

The ANOVA Summary Table for these data is shown
in Table 5.

As you can see, the only significant effect is the Outcome x
Esteem (O x E) interaction. The form of the interaction can be
seen in Figure 5.

Clearly the effect of "Outcome" is different
for the two levels of Esteem: For subjects high in self esteem,
failure led to less attribution to oneself then did success. By
contrast, for subjects low in self esteem, failure led to more
attribution to oneself than did success. Notice that the two lines
in the graph are not parallel. Nonparallel
lines indicate interaction. The significance test for the interaction
determines whether it is justified to conclude that the lines
in the population are not parallel. Lines do not have to
cross for there to be interaction.

Three-Factor Designs

Three-factor designs are analyzed in much the
same way as two-factor designs. Table 6 shows the analysis of
a study described by Franklin and Cooley investigating three factors
on the strength of industrial fans: (1) Hole Shape (Hex or Round),
(2) Assembly Type (Stake or Spun), and (3) Barrel Shape (knurled
or smooth). The dependent variable, breaking torque, was measured
in foot-pounds. There were eight observations in each of the eight
combinations of the three factors.

As you can see in Table 6, there are three main
effect, three two-way interactions, and one three-way interaction.
The degrees of freedom for the main effects are, as in a two-factor
design, equal to the number of levels of the factor minus one.
Since all the factors here have two levels, all the main effects
have one degree of freedom. The interaction degrees of freedom
are always equal to the product of the degrees of freedom of the
component parts. This holds for the three-factor interaction as
well as the two-factor interactions. The error degrees of freedom
is equal to the number of observations (64) minus the number of
groups (8) and is 56.

A three-way interaction means
that the two-way interactions differ as a function of the level
of the third variable. The usual way to portray a three-way
interaction is to plot the two-way interactions separately. Figure
5 shows the Barrel (knurled or smooth) x Assembly (Staked or
Spun) separately for the two levels of Hole Shape (Hex or Round).
For the Hex Shape, there is very little interaction with the
lines being close to parallel with a very slight tendency for
the effect of Barrel to be bigger for Staked than for Spun.
The two-way interaction for the Round Shape is different: The
effect of Barrel is bigger for Spun than for Staked. The finding
of a significant three-way interaction indicates that this difference
in two-way interactions is not due to chance.

Formatting data for Computer Analysis

The data in Table 3 have been reformatted in Table 7. Note how
there is one column to indicate the level of outcome and one column
to indicate the level of esteem. The coding is as follows:

High-self-esteem:1

Low self esteem: 2

Success: 1

Failure: 2

To use Analysis
Lab to do the calculations, you would copy the data and
then

- Click the "Enter/Edit User Data" button (You may
be warned that for security reasons you must use the keyboard
shortcut for pasting data).
- Paste your data.
- Click "Accept Data"
- Click the "Advanced" button next to the "Anova button."
- Select "attrib" as the dependent variable and both outcome
and esteem
as "group" variables.
- Click the "do ANOVA button."