Assessment of school evasion rates among undergraduate students using a discrete log-logistic regression model
DOI:
https://doi.org/10.5902/2179460X88599Keywords:
Discrete log-logistics, Regression, Evasion, EMVAbstract
School evasion, both in primary and secondary education, as well as in higher education, is certainly a problem that affects the results of the educational system in Brazil. In addition to being a problem that directly affects the country’s public spending, little is said about the subject and much less is there a policy to combat evasion. In search of some characteristics that explain the time until evasion occurs, one of the techniques that can be used is survival analysis. Traditionally, to model the time until the occurrence of an event of interest, continuous probability distributions are widely used. However, when this time is observed only in days, months or years, discrete probability distributions are more appropriate. To model and explain the dropout time of students in the Computer Science course at the Universidade Estadual da Paraíba – Campus I, we propose in this work the discrete Log-Logistic regression model. The parameters of the proposed model were estimated using the maximum likelihood method. To evaluate the accuracy of the estimators, the bias and mean squared error of the estimators are calculated using Monte Carlo simulation, considering different sample sizes and censoring percentages. From the estimated coefficients, it was found that age, the secondary education institution, the form of entry and the student’s study time on the course are factors that influence the time until your evasion.
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