Cluster analysis divides data into groups (clusters) that are meaningful and useful. Meaningful groups are the goal, and then the clusters should capture the natural structure of the data.
Published in Chapter:
University Student Absenteeism: Factors and Profiles
Xavier M. Triado (Universitat de Barcelona, Spain), Pilar Aparicio-Chueca (Universitat de Barcelona, Spain), Joan Guàrdia-Olmos (Universitat de Barcelona, Spain), Natalia Jaría-Chacón (Universitat de Barcelona, Spain), Maribel Peró Cebollero (Universitat de Barcelona, Spain), and Amal Elasri Ejjaberi (Universitat de Barcelona, Spain)
Copyright: © 2014
|Pages: 13
DOI: 10.4018/978-1-4666-4458-8.ch023
Abstract
Work on university student absenteeism is an interesting topic that treats motivation problems and its important consequences, like dropout, but is not easy to measure. In this chapter, the authors make a revision of the concept and an empirical approach to the possible reasons of student absenteeism through multivariate analyses—which the students themselves believe to be justified—and those offered by the faculty members in the case of the authors’ big school (with nine studies and 12,000 students), of the authors’ university (with 70,000 students), in the authors’ country. The analysis was carried out on two samples (1,161 students and 181 professors), which indicates that the reasons offered by each population are not the same. Through a cluster analysis, it is possible to identify six student performance profiles, which sheds some light on understanding this fact and the opportunity to suggest some ways of action.