Searching for types of goals in a Conceptual Space of goal characteristics

Abstract

This paper introduces an approach to combine ideas from Gärdenfors' Conceptual Spaces Framework (CSF) [1] and Clustering Techniques to the domain of Psychometrics in general and Motivational Psychology in particular. In the goal-setting literature of the last 50 years, scientists have postulated and empirically confirmed a wide variety of goal types with specific characteristics. We aim to reproduce such goal types, in line with CSF, as convex regions in quality dimensions. The data we use originates from an ongoing field study with a digital study assistant (DSA) for goal-setting in higher education and holds goals in natural language (= 637), formulated by university students (= 38), each related to scores for 32 goal characteristics, assessed with the Goal Characteristics Questionnaire (GCQ) [2]. The method we apply in this paper is searching for multi-peaked distributions by visual inspection of violin plots, scatter plots, and kernel density estimation plots (KDE) of single characteristics and two-dimensional permutations. If there are differences in data density in dimensions, applying clustering algorithms in these dimensions is worth the computation time. The results show multi-peaked distributions, while no non-overlapping convex clusters are evident by visual inspection. In the KDE plots, summits of high density exist, which are prospective candidates for convex regions, aka types. The findings encourage us to proceed in the endeavor and apply clustering algorithms in future studies, which may allow us to reproduce previous findings of goals and their characteristics with a novel method, apply and test the CSF on real-world data, and possibly derive new insights into the nature of educational goals. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).