FACTORS ASSOCIATED WITH PERFORMANCE ON TESTS “SABER PRO” OF CIVIL ENGINEERING STUDENTS OF COLOMBIAN UNIVERSITIES
DOI:
https://doi.org/10.15649/2346075X.234Keywords:
Saber Pro, Civil Engineering, Statistical analysis, Contingency tables, Correspondence analysis.Abstract
Introduction: Academic tests “Saber pro” are official and mandatory form part of a group of instruments used by the colombian government to assess the quality of the educational service both in the private and public sectors. They are also used as a selection tool for calls in graduate fellowships and/or recruitment in various companies. Materials and Methods: Results from “Saber Pro” test from last semester students of civil engineering nationwide were analyzed in this study, to determine the factors that influence scores. Different variables, qualitative and quantitative, associated with the score obtained by statistical methods such as contingency tables and analysis of simple and multiple correspondence were studied. Results and Discussion: The Chi-square test supports the independence of working variables (p = 0.858) and title (p = 0.433) in association with the score variable, indicating that neither a high school diploma earned by the student, or if the individual works or not, are related to the results obtained in the tests. In contrast to the variables gender (p = 0.000), type (p = 0.000), education parent (p = 0.000), mother’s education (p = 0.000) and stratum (p = 0.000), we conclude that they do play a role results. Conclusions: Correspondence analysis allows students to conclude that the higher than average scores are associated with male students, studying Civil Engineering at public institutions, have parents with college and living in social strata 4, 5 or 6. Moreover it is important to develop new studies where other variables that might explain the results and the implementation of other statistical models are analyzed as: discriminant analysis, principal component analysis and logistic regression analysis.
do play a role results. Conclusions:
References
(1) ICFES. Icfes mejor saber. [En línea] 15 de Agosto de 2014. [Citado el: 20 de Agosto de 2014.] Disponible en http://www.icfes.gov.co/examenes/saber-pro/ informacion-general.
(2) Díaz Monrroy, L G y Morales Rivera, M A. Análisis estadístico de datos categóricos. Bogotá: Universidad Nacional de Colombia, 2009.
(3) Peña, D. Análisis de datos multivariantes. Madrid: McGrawHill, 2002.
(4) Greenacre, M. La práctica del análisis de correspondencias. Barcelona: Fundación BBVA, 2008.
(5) Díaz Monrroy, L G. Estadística multivariada: Inferencia y métodos. Bogotá: Universidad Nacional de Colombia, 2007.
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