  In this paper, we introduce the application of Information Bottleneck as a
method to investigate properties of questionnaires developed for the study of
motivational profiles based on self-determination theory. Founded on
information theory, the Information Bottleneck method compresses
multidimensional categorical data into clusters by minimizing the loss of
relevant information. It does not require linearity such as Pearson
correlation. For exploratory data analysis, it allows investigating the
structure of questionnaire items grouping with a hierarchical partition as
perceived by the sample, based only on conditional probability distributions
calculated from answers to the questionnaire items. We applied the approach to
an instrument adapted from the academic motivation scale. The Information
Bottleneck approach showed a partition of items aligned with the
self-determination continuum. The results of this first application of
Information Bottleneck to psychometric studies suggested that this procedure
can could be useful as a consistent, complementary analysis for traditional
exploratory procedures such as factor analysis.
