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Activity Number: 503
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #321181 View Presentation
Title: A Finite Mixture Model Approach on the First-Year University Drop-Out Probability
Author(s): Matilde Bini* and Lucio Masserini
Companies: Universita Europea di Roma and University of Pisa
Keywords: Finite mixture of regression models ; University drop-out
Abstract:

University drop-out is one of the most important problems occurring in degree courses. The aim of this study is to analyze the first year university drop-out at the University of Pisa (Italy). In particular, we are focused on identifying the covariates affecting the response variable (if the student dropped out or not), and on detecting unobserved subgroups of students, if they exist, having different probabilities of dropping out. To perform this analysis we propose the use of a finite mixture logit model that allow us to consider a methodological framework where the population is made up by an unknown but finite number of subpopulations (latent classes). A dataset of the University of Pisa formed by administrative data, collected at enrolment time, and information collected by a survey on students' career after the first year of enrolment for the academic years 2011-2013, is used for this purpose. The analysis is focused on students of the first cycle degree courses. The characteristics detected of subgroups and the influential covariates, should represent useful information for the implementation of academic policy changes that could affect the drop-out rate.


Authors who are presenting talks have a * after their name.

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