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The tests of between-subjects effects help you to determine the
significance of a factor. However, they do not indicate how
the levels of a factor differ. The post hoc tests show the
differences in model-predicted means for each pair of
factor levels.
The first column displays the different post hoc tests.
The
next two columns display the pair of factor levels being tested.
When significance value for
the difference in Amount spent
for a pair of factor levels is less than 0.05,
an asterisk (*) is printed by the difference.
In this case, "biweekly" customers spend significantly
less than other customers.
"Weekly" and "sale" customers do not
spend significantly different amounts from each other.
Tamhane's T2 is generally more appropriate than Tukey's HSD
when there are unequal cell sizes, but the results in this case
are largely the same.
The confidence intervals for Tamhane's T2 are only slightly wider than
those for Tukey's HSD.
Since the results of these two tests
are not very different, it is safe for you to look at the results
of the homogenous subsets, which are available for Tukey's HSD
but not for Tamhane's T2.
The homogenous subsets table takes the results of the
post hoc tests and shows them in a more easily interpretable form.
In the subset columns the factor levels that do not have significantly
different effects are displayed in the same column.
In this
example, the first subset contains the "biweekly" customers.
The second subset contains the "weekly" and "often" customers.
The post hoc tests suggest that customers who shop more often than
once every other week will spend more, but effort at enticing
customers to shop more than once a week is wasted because they will
not spend significantly more.
However, the post hoc test results
do not account for the levels of other factors,
thus ignoring the possibility of an interaction effect with
Gender seen in the descriptive
statistics table. See the estimated marginal means to see how this
might change your conclusions.
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Post Hoc Tests |