Version classiqueVersion mobile
OpenEdition Books

Socioeconomic Factors and Outcomes in Higher Education

Carlos Felipe Rodríguez Hernández


Texte intégral

Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. In Salkind, N.J. Encyclopedia of measurement and statistics (pp. 651-657). Sage Publications.

Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, New Jersey: Wiley-Interscience.

Barrientos, J. (2008). Calidad de la educación pública y logro académico en Medellín 2004-2006. Una aproximación por regresión intercuartil. Lecturas de Economía, 68 (68), 121-144.

Bell, B. A., Ferron, J. M., & Kromrey, J.D. (2008). Cluster size in multilevel models: the impact of Sparse Data Structures on Point and Interval Estimates in Two-Level models. JSM Proceedings, Section on Survey Research Methods, 1122-1129.

Benzécri, J. (1979). Sur le calcul des taux dʼinertie dans lʼanalyse dʼun questionnaire. Cahiers de lʼAnalyse des Données, 4 (3), 377-378.

Castañeda, T. (2005). Targeting Social Spending To The Poor With Proxy-Means Testing: Colombiaʼs sisben System, Social Protection Unit. Human Development Network. The World Bank. Retrieved from:

Coleman, J., Campbell, E., Hobson, C., McPartland, F., Mood, A., & Weinfeld, F. (1966). Equality of educational opportunity. Washington D.C.: U.S. Government.

Correa, J.J. (2004). Determinantes del Rendimiento Educativo de los Estudiantes de Secundaria en Cali: un análisis multinivel. Revista Sociedad y Economía, 6, 81-105.

Delgado-Ramírez, M. B. (2013). Test on the quality of higher education–saber pro–What do the results indicate? Revista Colombiana de Anestesiología, 41 (3), 177-178.

DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation, 14 (20), 1-11.

Duncan, O. D., Featherman, D.L., & Duncan, B. (1972). Socioeconomic background and achievement. New York: Seminar Press.

Eurydice Network. (2009). National Testing of Students in Europe: Objectives, Organization and Use of Results. Retrieved from:

Field, A. (2009). Discovering statistics using spss (3rd ed.). London: Sage publications.

Fox, J. (1991). Regression Diagnostics: An Introduction. Newbury Park, Calif.: Sage Publications.

Gamoran, A., & Long, D. A. (2007). Equality of Educational Opportunity A 40 Year Retrospective. In Teese, R., Lamb, S., & Duru-Bellat, M. (Eds.), International studies in educational inequality, theory and policy (pp. 23-47). Dordrecht: Springer.

Gaviria, A. & Barrientos, J. (2001a). Calidad de la Educación y Rendimiento Académico en Bogotá. Coyuntura Social-Fedesarrollo, 24, 111-126.

Gaviria, A. & Barrientos, J. (2001b). Características del Plantel y Calidad de la Educación en Bogotá. Coyuntura Social-Fedesarrollo, 25, 81-98.

Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). New Jersey: John Wiley & Sons.

Greenacre, M. (1993). Correspondence Analysis in Practice. London: Academic Press.

Heck, R., & Thomas, S. (2000). An Introduction to Multilevel Modelling Techniques. New Jersey: Lawrence Erlbaum Associates Publishers.

Heiberger, R., & Holland, B. (2004). Statistical analysis and data display: An intermediate course with examples in S-plus, R, and SAS. New York: Springer.

ibm Corporation. (2016a, April 1). Symmetric and directional measures [literally] (Repository). ibm Knowledge Center, Retrieved from:

ibm Corporation. (2016b, April 1). Wilks’Lambda (Repository). ibm Knowledge Center, Retrieved from:

ibm Corporation. (2016c, April 1). Checking Homogeneity of Covariance (Repository). ibm Knowledge Center, Retrieved from:

ibm Corporation. (2016d, April 1). Factor Analysis Scores/Anderson-Rubin Method (Repository). ibm Knowledge Center, Retrieved from:

Jones, K., & Subramanian, S. (2013). Developing multilevel models for analyzing contextuality, heterogeneity and change using MLwiN 2.2. (Vol. 2). Bristol University, Retrieved from:

Kang, L., & Tian, L. (2013). Estimation of the volume under the roc surface with three ordinal diagnostic categories. Computational Statistics & Data Analysis, 62, 39-51.

Kapasný, J., & Rezác, M. (2013). Three-way roc analysis using SAS software. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61 (7), 2269-2275.

Kellaghan, T., Greaney, V., & Murray, T. (2009). Using the Results of a National Assessment of Educational Achievement. Washington. D.C.: The World Bank.

Kutner, M., Nachtsheim, C., Neter, J., & Li, W. (2005). Applied Linear Statistical Models. New York: McGraw-Hill.

Li, G., Chen, W., & Duanmu, J. L. (2010). Determinants of International Students’Academic Performance: A Comparison between Chinese and Other International Students. Journal of Studies in International Education, 14 (4), 389-405.

Mass, C., & Hox, J. (2003). The influence of violations of assumptions on multilevel parameter estimates and their standard error. Computational Statistics & Data Analysis, 46 (3), 427-440.

Mayers, A. (2013). Introduction to Statistics andspss in Psychology. New Jersey: Prentice Hall.

McKenzie, K., & Schweitzer, R. (2001). Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher Education Research and Development, 20 (1), 21-33.

Michailidis, G., & deLeeuw, J. (1998). The Gifi System of Descriptive Multivariate Analysis. Statistical Science, 13 (4), 307-336.

Morris, A. (2011). Student Standardised Testing: Current Practices in oecd Countries and a Literature Review. oecd Education Working Papers, 65, New Yersey: oecd Publishing.

Musso, M., Kyndt, E., Cascallar, E., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1 (1), 42-71.

Nimon, K.F. (2012). Statistical assumptions of substantive analyses across the general linear model: a mini-review. Frontiers in psychology, 3 (322), 1-5.

Osborne, J., & Waters, E. (2002). Four Assumptions of Multiple Regression that Researchers should always test. Practical Assessment, Research and Evaluation. Retrieved from:

Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park: Sage.

Rovai, A. P., Baker, J. D., & Ponton, M. K. (2013). Social science research design and statistics: A practitioner’s guide to research methods and ibm spss. Watertree Press llc.

Sharma, S. (1996). Applied Multivariate Techniques. New York: John Wiley & Sons Inc.

Sirin, S. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research, 75 (3), 417-453.

Starkweather, J. & Herrington, R. (2014). Correspondence Analysis (Repository). University of North Texas (Research and Statistical Support). Retrieved from:

Starkweather, J., & Moske, A. (2011). Research and Statistical Support. Retrieved from:

Tabachnick, B., & Fidell, L. (2001). Using Multivariate Statistics (5th ed.). Boston: Pearson.

Tacq, J. (1997). Multivariate Analysis Techniques in Social Science Research. London: SAGE Publications Ltd.

The World Bank. (2012). Reviews of National Policies for Education: Tertiary Education in Colombia 2012. Retrieved from: 20Colombia%202012.pdf

ucla (2014). Introduction to sas. ucla: Academic Technology Services, Statistical Consulting Group. Retrieved from:

Wang, L., Beckett, G., & Brown, L. (2006). Controversies of Standardized Assessment in School Accountability Reform: A Critical Synthesis of Multidisciplinary Research Evidence. Applied Measurement in Education, 19 (4), 305-328.

White, K. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91 (3), 461-481.

Wuensch, K. (2014). Discriminant Function Analysis with Three or More Groups (Lecture notes). East Carolina University (Karl Wuensch’s Statistics Lessons). Retrieved from:

Zwick, R. (2012). The Role of Admissions Test Scores, Socioeconomic Status, and High School Grades in Predicting College Achievement. Pensamiento Educativo. Revista de Investigación Educacional Latinoamericana, 2 (49), 23-30.

© Universidad externado de Colombia, 2016

Conditions d’utilisation :



open access

Offert par L’éditeur de ce site


Volume papier