SPSS: Profile Analysis

{Link to} Sample Data Set WAIS

*Scripts and more datasets for profile
analysis using SAS can be found at: http://psych.colorado.edu/.

The data set above is a good example of when and how to
use a profile analysis. The rest of this page will give an overview of how to
run a profile analysis using SPSS and the key outputs that are of interest.
There are other websites that give examples of how to run a profile analysis in
the same data set using SAS scripts.

Profile analysis datasets should be arranged so that the ‘repeated
measure’ for all groups are found in the same column; the groups can be
subdivided by a numbering scheme. Remember that the repeated measure can either
be the same test administered over a series of time points or multiple
different tests of the same measure.

A profile analysis can easily be accomplished using the
repeated measures module under GLM in SPSS (**Analyze****ŕ****General
Linear Model****ŕ**** Repeated
Measure**). Define the number of levels in the within
group factor by the number of subtests (or ‘repeated measures’). The column
defining the subject groups is the between subject factor. Under **plots** select the subtests to be on the
horizontal axis and the groups column to be under
individual lines; this will generate your profile plots. Additional statistics
can be selected; descriptive statistics and homogeneity of variance test are
important in order to ensure test assumptions are met.

The
tests of between subject effects shows that there is a significant difference
scores between the senile and non-senile groups (averaged across all subtests),
this essentially suggests a difference in levels. It does not show what direction
the difference is in or if the difference is the same across all tests. The
same thing is true for the within-subject effects; there is a significant
effect between the different subtests within one group. The statistic does not
indicate if one or all of the subtests differ. Furthermore, SPSS automatically
generates ‘contrasts’ by fitting lines (linear, quadratic, cubic functions) to
data; this is usually not a very informative contrast. More often than not
contrasts require reanalysis following a transformation of the dataset;
contrasts may require a multivariate or univariate statistic.

Profile
plots are the most informative output to look at after determining the data
adequately meet the assumptions. Datasets that require the use of profile analysis
are usually complicated enough that the between subject, within subject
effects, and contrasts generated in the SPSS output give only limited
information and do not direct you to the more subtle effects that are present.

In
the profile below, there is a clear difference in level across all points, the
senile patients scored lower (p=0.0001). The profile plot shows that the
difference between the two groups lies primarily on the high scores of the
non-senile group on subtest 1 and subtest 3 and the poor performance of the
senile group on test 2 and 4. Contrasts may then be planned to help better
describe this difference present between these subtests.

There
is also a clear difference between different subtests within the same group. A
look at the profile plot indicates that subtest 1and 3 and 2 and 4 may not be
different from each other. Depending on the research question of interest the
two subtests might be pooled or pulled out to test for difference using a
simple contrast.

Profile plots are a great tool to help manage complicated
multivariate data sets. The direct you to the most relevant contrasts and
statistical tests to make when a simple test of group difference does not
suffice.