Academic performance of students from entrance to graduation via quasi U-statistics: a study at a Brazilian research university

Author(s)

Maia, Rafael Pimentel ; Pinheiro, Hildete Prisco ; Pinheiro, Aluisio

• Document type: Journal article
• Source: Journal of Applied Statistics; v. 43, n. 1, p. 15-pg., 2016-01-02.

Abstract

We present novel methodology to assess undergraduate students' performance. Emphasis is given to potential dissimilar behaviors due to high school background and gender. The proposed method is based on measures of diversity and on the decomposability of quasi U-statistics to define average distances between and within groups. One advantage of the new method over the classical analysis of variance is its robustness to distributional deviation from the normality. Moreover, compared with other nonparametric methods, it also includes tests for interaction effects which are not rank transform procedures. The variance of the test statistic is estimated by jackknife and p-values are computed using its asymptotic distribution. A college education performance data is analyzed. The data set is formed by students who entered in the University of Campinas, Brazil, between 1997 and 2000. Their academic performance has been recorded until graduation or drop-out. The classical ANOVA points to significant effects of gender, type of high school and working status. However, the residual analysis indicates a highly significant deviation from normality. The quasi U-statistics nonparametric tests proposed here present significant effect of interaction between type of high school and gender but did not present a significant effect of working status. The proposed nonparametric method also results in smaller error variances, illustrating its robustness against model misspecification. (AU)

• FAPESP's process: 11/15047-7 - Applications of diversity measures and quasi U-Statistics
• Grantee: Hildete Prisco Pinheiro
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 13/00506-1 - Time series, wavelets and functional data analysis
• Grantee: Pedro Alberto Morettin
• Support Opportunities: Research Projects - Thematic Grants