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Prediction of University Students' Academic Achievement by Linear and Logistic Models

Prediction of University Students' Academic Achievement by Linear and Logistic Models University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Spanish Journal of Psychology Cambridge University Press

Prediction of University Students' Academic Achievement by Linear and Logistic Models

The Spanish Journal of Psychology , Volume 11 (1): 14 – Apr 10, 2014

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References (56)

Publisher
Cambridge University Press
Copyright
Copyright © Cambridge University Press 2008
ISSN
1988-2904
eISSN
1138-7416
DOI
10.1017/S1138741600004315
Publisher site
See Article on Publisher Site

Abstract

University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns.

Journal

The Spanish Journal of PsychologyCambridge University Press

Published: Apr 10, 2014

Keywords: logistic versus linear regression; prediction; credits; academic achievement; advance in career; regresión logística versus regresión lineal; predicción; créditos; rendimiento académico; avance en la carrera

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