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 (AnalyzeŕGeneral Linear Modelŕ 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.