Analysis of multivariate data like gallup questionnaires can be truly difficult, if the number of data fields is big. For instance, direct groupings of the opinions of human beings are often meaningless if all available data fields are used, due to the large number of value combinations (see [6]). Also, the number of data records is usually small due to the work needed for collecting and processing them. Unless the questionnaire or the requested answers are trivial, there is no one-stage method available that gives satisfactory results. For example, the use of the Self-Organizing Map [2, 3] might only discriminate between well and badly filled questionnaires, since the large amount of categories prevents clear interpretations of the clustering objectives.
If the questions are divided into a few small categories, analysing them separately and combining the results can produce better results. This kind of procedure also allows the addition of prior knowledge of the data to the analysis. The use of several SOMs is partially adopted from [1], where the main idea was to create linked graphical representations of multi-categorical data.