Difference Between Means, Correlated Pairs
Prerequisites
Hypothesis
Testing,
Testing a Single Mean,
t Distribution, Values of the Pearson Correlation, Difference
Between Two Means (Independent Groups)
Learning Objectives
 Determine whether you have correlated pairs or independent groups
 Compute a t test for correlated pairs
Let's consider how to analyze the data from the
ADHD treatment case study.
These data consist of the scores of 24 children with ADHD on
a delay of gratification (DOG) task. Each child was tested under
four dosage levels. In this section we will be concerned only
with testing the difference between the mean in the placebo
(D0) condition and mean in the highest dosage condition (D60).
The first question is why the difference between means should
not be tested using the procedure described in the section Difference
Between Two Means (Independent Groups). The answer lies
in the fact that in this experiment we do not have independent
groups. The scores in the D0 condition are from the same subjects
as the scores in the D60 condition. There is only one group
of subjects, each subject being tested in both the D0 and D60
conditions.
Figure 1 shows a scatterplot of the 60 mg scores
(D60) as a function of the 0 mg scores (D0). It is clear that
children who get more correct in the D0 condition tend to get
more correct in the D60 condition. The correlation between the
two conditions is high: r = 0.80. Clearly these two variables
are not independent.
Computations
You may recall that the method to test the difference
between these means was presented in the section on "Testing
a Single Mean." The computational procedure is to compute the difference
between the D60 and the D0 condition for each child and test
whether the mean difference is significantly different from
0. The difference scores are shown in Table 1. As shown in
the section on testing a single mean, the mean difference
score is 4.96 which is significantly different from 0: t =
3.22, df = 23, p = 0.0038. This t test has various names including "correlated
t test" and "relatedpairs
t test."
In general, the correlated t test is computed by first computing the differences between the two scores for each subject. Then, a test of a single mean is computed on the mean of these difference scores.
If you had mistakenly used the method for an independentgroups
t test with these data, you would have found that t = 1.42, df
= 46, and p = 0.15. That is, the difference between means would
not have been found to be statistically significant. This is a
typical result: correlated t tests almost always have greater
power than independent ttests. This is because in correlated
t tests, each difference score is a comparison of performance
in one condition with the performance of that same subject in
another condition. This makes each subject "their own control"
and keeps differences between subjects from entering into the
analysis. The result is that the standard error of the difference
between means is smaller in the correlated t test and, since this
term is in the denominator of the formula for t, results in a
larger t.
Details about the Standard Error of the Difference
between Means (Optional)
To see why the standard
error of the difference between means is smaller in a correlated
t test, consider the variance of difference scores. As shown in
the section on the Variance
Sum Law, the variance of the sum or difference of the two
variables X and Y is:
Therefore, the variance
of difference scores is the variance in the first condition (X)
plus the variance in the second condition (Y) minus twice the
product of (1) the correlation, (2) the standard deviation X,
and (3) the standard deviation of Y. For the current example,
r = .80 and the variances and standard deviations are shown in
Table 1.
The variance of the difference scores of 56.82
can be computed as:
128.02 + 151.78  (2)(0.80)(11.31)(12.32)
which is equal to 56.82 except for rounding
error. Notice that the higher the correlation, the lower the standard
error of the mean.
