1,721,010 research outputs found
Binomial Mixture Modeling of University Credits
No full textThe paper reviews finite mixture models for binomial counts with concomitant variables. These models are well known in theory, but they are rarely applied. We use a binomial finite mixture to model the number of credits gained by freshmen during the first year at the School of Economics of the University of Florence. The finite mixture approach allows us to appropriately account for the large number of zeroes and the multimodality of the observed distribution. Moreover, we rely on a concomitant variable specification to investigate the role of student background characteristics and of a compulsory pre-enrollment test in predicting gained credits. In the paper, we deal with model selection, including the choice of the number of components, and we devise numerical and graphical summaries of the model results in order to exploit the information content of the concomitant variable specification. The main finding is that the introduction of the pre-enrollment test gives additional information for student tutoring, even if the predictive power is modest. © 201
University admission test and students’ careers: evidence from the School of Economics in Florence
No full textIn the academic year 2008/2009, the School of Economics of the University
of Florence introduced a compulsory test to evaluate the background of the students
wishing to enrol in a degree program. In this paper, we assess the predictive power
of the test score in terms of number of gained credits, making comparisons with the
predictive power of variables recorded in administrative data, such as the type of high
school and the high school final grade. To disentangle direct and indirect effects, the
result of the admission test is treated as an intermediate variable in a regression chain
graph. About 20% of the enrolled student did not gain any credit at the end of the first
year, thus we consider a two-part (hurdle) model, in order to deal correctly with excess
zeros
University admission test and students' careers: an analysis through a regression chain graph with a hurdle model for the credits
No full textStatistical modelling of gained university credits to evaluate the role of pre-enrolment assessment tests: An approach based on quantile regression for counts
No full textConsidering the case of the School of Economics of the University of Florence, the paper investigates whether the pre-enrolment assessment test is an effective tool to predict student performance. The analysis is tailored to evaluate the additional information yielded by the test beyond the background characteristics of the candidates already available from administrative records, such as the high school type and final grade. The student performance is measured by the number of gained credits after one year, which is a count variable with an irregular distribution and a peak in zero. These features pose a challenge in statistical modelling, which is solved by a two-part model with a logit specification for the zeros, while positive values are analyzed by quantile regression for counts. To disentangle direct and indirect effects of background variables, the result of the pre-enrolment assessment test is treated as an intermediate variable in a regression chain graph. The results show that the pre-enrolment test adds some information to predict student performance, which can be exploited for tutoring
The use of permutation tests for variance components in linear mixed models
No full textStandard asymptotic chi-square distribution of the likelihood ratio and score statistics under the null hypothesis does not hold when the parameter value is on the boundary of the parameter space. In mixed models it is of interest to test for a zero random effect variance component. Some available testsfor the variance component are reviewed and a new test within the permutation framework is presented. The power and significance level of the differenttests are investigated by means of a Monte Carlo simulation study. The proposed test has a significance level closer to the nominal one and it is more powerful
Conditioning on the Pre-Test versus Gain Score Modelling: Revisiting the Controversy in a Multilevel Setting
No full textWe consider estimating the effect of a treatment on a given outcome measured on subjects tested both before and after treatment assignment in observational studies. A vast literature compares the competing approaches of modelling the post-test score conditionally on the pre-test score versus modelling the difference, namely, the gain score. Our contribution lies in analyzing the merits and drawbacks of two approaches in a multilevel setting. This is relevant in many fields, such as education, where students are nested within schools. The multilevel structure raises peculiar issues related to contextual effects and the distinction between individual-level and cluster-level treatments. We compare the two approaches through a simulation study. For individual-level treatments, our findings align with existing literature. However, for cluster-level treatments, the scenario is more complex, as the cluster mean of the pre-test score plays a key role. Its reliability crucially depends on the cluster size, leading to potentially unsatisfactory estimators with small clusters
Growth models for the progress test in Italian dentistry degree programs
No full textIn 2017, for the first time in Italy, the students enrolled in Dental Schools performed a Progress test. The results allow us to analyse the evolution of students’ abilities during the course years. Furthermore, we can evaluate the coherence of the learning pattern with the core curriculum. We analyse the results of the Progress Test with a mixed-effects growth-curve binomial model for the number of correct answers, using fixed effects for the topics and random effects for the universities. The learning trajectories for each topic are modelled via polynomials of time. Using the Empirical Bayes predictions of random effects, we obtain the trajectories for the Italian universities, which show substantial heterogeneity in the starting levels and growth rates. The results give insights into inequalities in the educational process across Italian universities to be exploited for planning intervention
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