Employing latent profile analysis to identify student motivational profiles

Bachelor Thesis (2022)
Author(s)

P. Huisman (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Annoesjka Cabo – Mentor (TU Delft - Statistics)

Jacqueline Wong – Graduation committee member (TU Delft - Statistics)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Pauline Huisman
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Pauline Huisman
Graduation Date
08-07-2022
Awarding Institution
Delft University of Technology
Programme
Applied Mathematics
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Latent profile analysis is a statistical modeling approach used to identify hidden subpopulations (i.e., latent profiles) within a population. These latent profiles are identified based on values of observed continuous variables, also known as profile indicators. While LPA is getting more popular in education sciences and psychology to group people based on similar characteristics, very little is known about the mathematical formulation. In this thesis, the mathematical foundations of LPA is introduced and explained. This leads to a discussion on the assumptions for the model.
After investigating the mathematical foundations of LPA, we applied LPA to identify different profiles of motivation in a student population at Delft University of Technology. We used a set of survey data measuring four types of motivation (i.e., profile indicators). Results of the analysis showed that there are four different student motivational profiles, each consisting of a different combination of the four types of motivation.

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