Vol. 12 No. 1 (2021): Revista Cambios y Permanencias
Artículos

Socioeconomic and cultural characteristics in relation to the academic situation of the students of the Technological University of Pereira: 2015-2019

Patricia Carvajal
Universidad Tecnológica de Pereira
Bio
René Guevara Ramírez
Universidad Pedagógica Nacional
Bio

Published 2021-06-30

Keywords

  • Statistics,
  • correspondence analysis,
  • logistic regression,
  • objectivity

How to Cite

Carvajal, P., & Guevara Ramírez, R. (2021). Socioeconomic and cultural characteristics in relation to the academic situation of the students of the Technological University of Pereira: 2015-2019. Cambios Y Permanencias, 12(1), 522–559. Retrieved from https://revistas.uis.edu.co/index.php/revistacyp/article/view/12397

Abstract

This article is part of an investigation in which qualitative and quantitative research methods have been embedded in order to fully understand the functioning of a social fact, such as permanence in higher education. In particular, verifying the academic situation of the students of the Technological University of Pereira (UTP) in relation to their conditions of origin, evidenced in their social, economic and cultural characteristics. Here the use of quantitative tools is shown to objectify the analyzes, looking for statistically graspable regularities, so that scientific interpretations based on objectivity and not on the researcher's assumptions can be elaborated. That is, by confronting hypotheses with real data collected from observation. For this purpose, the variables obtained through the interview of the integrated early warning system will be included, applied to the students upon their admission to the UTP and with which the risk of desertion is established. As well as those that rest in the Registry and Control databases and give an account of the results in the construction of the academic trajectory of these young people. The period under study is between 2015-2019 and the following are used: descriptive statistics, Simple Correspondence Analysis (ACS) and Multinomial Logistic Regression (RLM).

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