Prediction of University students' academic achievement by linear and logistic models

• Published in: The Spanish journal of psychology Vol. 11; no. 1; p. 275
• Main Authors: Ayan, Maria Noel Rodriguez, Garcia, Maria Teresa Coello
• Format: Journal Article
• Language: Spanish
• Published: Universidad Complutense de Madrid 01-05-2008
• Subjects: Students; Universities and colleges
• ISSN: 1138-7416

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. Keywords: logistic versus linear regression, prediction, credits, academic achievement, advance in career Se estudia el efecto de dos tipos de factores sobre el rendimiento de estudiantes universitarios: variables academicas de rendimiento previo y variables demograficas, mediante modelos lineales y logfsticos. El principal objetivo del trabajo es comparar los resultados obtenidos con ambas tecnicas estadfsticas, para determinar cual de ellos es mas adecuado en terminos de ajuste y capacidad predictiva cuando se pretende explicar y predecir el rendimiento academico, en funcion de variables de rendimiento previo y factores sociodemograficos. Como medida del rendimiento a predecir se empleo el avance en la carrera. Las hipotesis planteadas son: 1) El avance esta directamente relacionado con las calificaciones en materias basicas de primer ano y con variables demograficas y 2) Los modelos logfsticos son mas adecuados que los modelos lineales, ya que presentan mayor capacidad predictiva. Los resultados permiten confirmar la primera hipotesis en su primera parte, ya que el rendimiento previo esta directa y significativamente asociado al avance en la carrera. Pero se cumple de forma parcial por lo que se refiere al efecto factores demograficos. Con respecto a la segunda hipotesis, la regresion logfstica mostro ser mas adecuada que la lineal, pues arroja estimaciones mas estables en relacion con la presencia de patrones de mal ajuste. Palabras clave: regresion logistica versus regresion lineal, prediccion, creditos, rendimiento academico, avance en la carrera