Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-30T09:15:03.441Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  10 April 2014

Maria Noel Rodríguez Ayán  and
Maria Teresa Coello García
Maria Noel Rodríguez Ayán*
Affiliation:
Universidad de la República (Uruguay)
Maria Teresa Coello García
Affiliation:
Universidad Complutense de Madrid
*
Correspondence concerning this article should be addressed to Maria Noel Rodríguez Ayán, Facultad de Química, CC 1157, Gral. Flores 2124 CP 11800, Montevideo(Uruguay). Phone/Fax: 5982 – 929-0770. E-mails: [email protected]and, [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Se estudia el efecto de dos tipos de factores sobre el rendimiento de estudiantes universitarios: variables académicas de rendimiento previo y variables demográficas, mediante modelos lineales y logísticos. El principal objetivo del trabajo es comparar los resultados obtenidos con ambas técnicas estadísticas, para determinar cuál de ellos es más adecuado en términos de ajuste y capacidad predictiva cuando se pretende explicar y predecir el rendimiento académico, en función de variables de rendimiento previo y factores sociodemográficos. Como medida del rendimiento a predecir se empleó el avance en la carrera. Las hipótesis planteadas son: 1) El avance está directamente relacionado con las calificaciones en materias básicas de primer año y con variables demográficas y 2) Los modelos logísticos son más adecuados que los modelos lineales, ya que presentan mayor capacidad predictiva. Los resultados permiten confirmar la primera hipótesis en su primera parte, ya que el rendimiento previo está directa y significativamente asociado al avance en la carrera. Pero se cumple de forma parcial por lo que se refiere al efecto factores demográficos. Con respecto a la segunda hipótesis, la regresión logística mostró ser más adecuada que la lineal, pues arroja estimaciones más estables en relación con la presencia de patrones de mal ajuste.

Keywords

logistic versus linear regressionpredictioncreditsacademic achievementadvance in careerregresión logística versus regresión linealprediccióncréditosrendimiento académicoavance en la carrera
Type
Articles
Information
The Spanish Journal of Psychology , Volume 11 , Issue 1 , May 2008 , pp. 275 - 288
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agresti, A. (1984). Analysis of ordinal categorical data. New York: Wiley.Google Scholar
Agresti, A. (1990). Categorial data analysis. New York: Wiley.Google Scholar
Alexander, F.K. (2000). The changing face of accountability: Monitoring and assessing institutions in higher education. Journal of Higher Education, 71, 411–430.Google Scholar
Ananth, C. & Kleinbaum, D (1997). Regression models for ordinal responses: A review of methods and applications. International Journal of Epidemiology, 26, 1323–1333.CrossRefGoogle ScholarPubMed
Anaya, G. (1999). College impact on student learning: Comparing the use of self-reported gains, standardized test scores and college grades. Research in Higher Education, 40, 499–526.CrossRefGoogle Scholar
Biggs, J. (1989). Approaches to the enhancement of tertiary teaching. Higher Education Research and Development, 8, 7–25.CrossRefGoogle Scholar
Bivin, D., & Rooney, P. (1999). Forecasting credit hours. Research in Higher Education, 40, 613–632.CrossRefGoogle Scholar
Burke, J., Modarresi, S., & Serban, A. (1999). Performance: Shouldn't it count for something in state budgeting? Change, 31, 16–23.CrossRefGoogle Scholar
Cassidy, S., & Eachus, P. (2000). Learning style, academic belief systems, self-report student proficiency and academic achievement in higher education. Educational Psychology, 20, 3118–322.CrossRefGoogle Scholar
Clifton, R., Perry, R., Adams, C., & Roberts, L. (2004). Faculty environments, psychological dispositions and the academic achievement of college students. Research in Higher Education, 45, 801–829.CrossRefGoogle Scholar
Cumsille, F., & Bangdiwala, S. (2000). Categorización de variables en el análisis estadístico de datos: consecuencias sobre la interpretación de resultados. Revista Panamericana de Salud Pública/Pan American Journal of Public Health, 8, 348–354.CrossRefGoogle Scholar
De la Orden, A., Oliveros, L., Makofozi, J., & González, C. (2001). Modelos de investigación del bajo rendimiento. Revista Complutense de Educación, 12, 159–178.Google Scholar
DeMaris, A. (2002). Explained variance in logistic regression. Sociological Methods and Research, 31, 27–74.CrossRefGoogle Scholar
Elmore, P., & Woehlke, P. (1998). Twenty years of research methods employed in American Educational Research Journal, Educational Researcher and Review of Educational Research. Presented in the Annual Meeting of the American Educational Research Association, San Diego, California.Google Scholar
García, J. (1986). El análisis discriminante y su utilización en la predicción del rendimiento académico. Revista de Educación, 280, 229–252.Google Scholar
García, M., Alvarado, J., & Jiménez, V. (2000). La predicción del rendimiento académico: regresión lineal versus regresión logística. Psicothema, 12, 248–252.Google Scholar
Goberna, M., López, M., & Pastor, J. (1987). La predicción del rendimiento como criterio para el ingreso en la Universidad. Revista de Educación, 283, 235–248.Google Scholar
Goenner, C., & Snaith, S. (2004). Accounting for model uncertainty in the prediction of university graduation rates. Research in Higher Education, 45, 25–41.CrossRefGoogle Scholar
Goodwin, L., & Goodwin, W. (1985). Statistical techniques in ERJ articles: 1979-1983: The preparation of graduate students to read the educational research literature. Educational Researcher, 14, 5–11.Google Scholar
Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
Harackiewicz, J.Barron, K., & Elliot, A. (1998). Rethinking achievement goals: When are they adapted for College students and why? Educational Psychologist, 33, 1–21.CrossRefGoogle Scholar
Hosmer, D., & Lemeshow, S. (1989). Applied logistic regression. New York: Wiley & Sons.Google Scholar
House, J., Hurst, R., & Keely, E. (1996). Relationship between learner attitudes, prior achievement and performance in a General Education Course: A multi-Institutional study. International Journal of Instructional Media, 23, 257–271.Google Scholar
Hutchinson, S., & Lovell, C. (2004). A review of methodological characteristics of research published in key journals in Higher Education. Research in Higher Education, 45, 383–403.CrossRefGoogle Scholar
Jiménez, C. (1987). Rendimiento académico en la universidad a distancia. Un estudio empírico sobre su evolución y predicción (II). Revista de Educación, 284, 317–347.Google Scholar
Kelly, E., Holloway, R., & Chapman, D. (1981). Prediction of achievement for high school students in college courses. Journal of Educational Research, 75, 5–15.CrossRefGoogle Scholar
Kieffer, K., Reese, R., & Thompson, B. (2001). Statistical techniques employed in AERJ and ICP articles from 1988 to 1997: A methodological review. Journal of Experimental Education, 69, 280–309.CrossRefGoogle Scholar
Kuh, G., Bean, J., Bradley, R., & Coomes, M. (1986). Contributions of student affairs journals to the literature on college students. Journal of College Student Personnel, 27, 292–304.Google Scholar
Kuh, G., Bean, J., Bradley, R., Coomes, M., & Hunter, D. (1986). Changes in research on college students published in selected journals between 1969 and 1983. Review of Higher Education, 9, 177–192.CrossRefGoogle Scholar
Lundeberg, M., & Diemert, S. (1995). Influence of social interaction on cognition: Connected learning in science. Journal of Higher Education, 66, 312–335.Google Scholar
MacCallum, R., Zhang, S., Preacher, K., & Rucker, D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19–40.CrossRefGoogle ScholarPubMed
Makinen, J., & Olkinuora, E. (2004). University students' situational reaction tendencies: Reflections on general study orientations, learning strategies and success. Scandinavian Journal of Educational Research, 48, 478–491.CrossRefGoogle Scholar
Manor, O., Mathews, S., & Power, C. (2000). Dichotomous or categorical response? Analysing self-rated health and lifetime social class. International Journal of Epidemiology, 29, 149–157.CrossRefGoogle ScholarPubMed
Martínez, A. (1997). Understanding and investigating female friendship's educative value. Journal of Higher Education, 68, 119–159.Google Scholar
Mathiasen, R. (1984). Producing college academic achievement: A research review. College Student Journal, 18, 380–386.Google Scholar
McCullagh, P., & Nelder, J.A. (1989). Generalized linear models (2nd ed.). London: Chapman & Hall.CrossRefGoogle Scholar
McKenzie, K., & Schweitzer, R. (2001). Who succeeds at University? Factors predicting academic performance in first year Australian university students. Higher Education Research & Development, 20, 21–33.CrossRefGoogle Scholar
Micceri, T. (1989). The unicorn, the normal curve and other improbable creatures. Psychological Bulletin, 105, 156–166.CrossRefGoogle Scholar
Mouw, J., & Kahnna, R. (1993). Prediction of academic success: A review of the literature and some recommendations. College Student Journal, 27, 328–336.Google Scholar
Naylor, R., & Smith, J. (2002). Schooling effects of subsequent university performance: Evidence for the UK university population. Coventry, UK: Department of Economics, Warwick University. Retrieved September 20, 2005, from http://www2.warwick.ac.fac/soc/economics/research/papers/twerp657.pdf.Google Scholar
Noble, J., Davenport, M., Schiel, J., & Pommerich, M. (1999). Relationships between noncognitive characteristics, High School coursework and grades, and test scores of ACT-tested students. ACT Research Report Series, 99–4. Iowa City, IA: American College Testing Program.Google Scholar
Nonis, S., & Wright, D. (2003). Moderating effects of achievement striving and situational optimism on the relationship between ability and performance outcomes of college students. Research in Higher Education, 44, 327–346.CrossRefGoogle Scholar
Nurmi, J.E., Aunola, K., Salmela-Aro, K., & Lindroos, M (2003). The role of success expectation and task avoidance in academic performance and satisfaction: Three studies on antecedents, consequences and correlates. Contemporary Educational Psychology, 28, 59–90.CrossRefGoogle Scholar
Pardo, A., & Olea, J. (1993). Desarrollo cognitivo-motivacional y rendimiento académico en segunda etapa de EGB y BUP. Estudios de Psicología, 49, 21–32.Google Scholar
Pedhazur, E. (1997). Multiple Regression in Behavioral Research. Fort Worth, TX: Hartcourt Brace College.Google Scholar
Peng, C., So, T., Stage, F., & St. John, E. (2002). The use and interpretation of logistic regression in Higher Education Journals: 1988-1999. Research in Higher Education,43, 259–293.CrossRefGoogle Scholar
Pike, G., & Saupe, J. (2002). Does High School matter? Research in Higher Education, 43, 187–207.CrossRefGoogle Scholar
Remus, W., & Wong, C. (1982). An evaluation of five models for the admission decision. College Student Journal, 16, 53–59.Google Scholar
Sirin, S. (2005). Socioeconomic status and academic achievement: A meta-analytic review. Review of Educational Research, 75, 417–453.CrossRefGoogle Scholar
SPSS 11.0. (2001). SPSS Manual. Regression models. Chicago, IL: SPSS.Google Scholar
Stevens, J.P. (2001). Applied multivariate statistics for the social sciences. (4th ed.) Hillsdale, N.J: Erlbaum.CrossRefGoogle Scholar
Tabacnik, B.G., & Fidell, L.S. (1989). Using multivariate statistics. (2nd ed.). New York: Harper Collins.Google Scholar
Tinto, V. (1993). Leaving college: Rethinking the causes of and cures of student attrition. (2nd ed.), Chicago: University of Chicago Press.Google Scholar
Van den Berg, M.N., & Hofman, W.H.A. (2005). Student success in university education: A multimeasurement study of the impact of student and faculty factors on study progress. Higher Education, 50, 413–446.CrossRefGoogle Scholar
Wilson, R.L., & Hardgrave, B.C. (1995). Predicting graduate student success in an MBA program: Regression versus classification. Educational and Psychological Measurement, 35, 186–195.CrossRefGoogle Scholar
Volkwein, J.F, Carbone, D.A., & Volkwein, E.A. (1988). Research in Higher Education: Fifteen years of scholarship. Research in Higher Education, 28, 271–280.CrossRefGoogle Scholar
Yorke, M. (2004). Institutional research and its relevance to the performance of higher education institutions. Journal of Higher Education Policy and Management, 26, 141–152.CrossRefGoogle Scholar
Zeegers, P. (2004). Student learning in higher education: A path analysis of academic achievement in science. Higher Education Research and Development, 23, 35–56.CrossRefGoogle Scholar