Qualitative motivation with sets and relations

Abstract

In self-determination theory, multiple conceptual regulations of motivation are posited. These forms of motivation are especially qualitatively viewed, and there are situations in which combinations of those regulations occur. This can be modeled versatilely by sets and relations. I discuss discrete models from the theory of knowledge spaces for the multidimensional combinatorial conceptualization of motivation. This approach allows to also define the equal or distinct gradations of any regulation type, which may or may not be cumulative, in the sense that they do or do not entail linear orders, respectively. How to obtain such structures in practice is demonstrated by relational data mining. The technique applied is inductive item tree analysis, an established method of Boolean analysis of questionnaires. This technique was originally developed for knowledge space theory and in this paper is applied to motivation. From the scores of a data set on learning motivation, the motivation spaces and co-occurrence relations for the gradations of the basic regulation types are extracted, thus enumerating their potential sub-forms. This could help to differentiate specific forms of motivation that may otherwise be laborious or problematic to measure. In that empirical application, the underlying motivation spaces and relations were computed within each of the intrinsic, identified, introjected, and external regulations, in autonomous and controlled motivations, and in the entire motivation domain. Based on combinatorial structures, this paper also contributes to the qualitative assessment of the dimensionality of motivation in self-determination theory.