Abstract
This chapter is an attempt to demonstrate the use of the methodology to measure the school contribution to equity proposed in Chap. 4. Specifically, we present results that emerged from reanalysing the data of two effectiveness studies conducted in order to test the validity of the dynamic model of educational effectiveness. Each study is briefly presented and the process used to analyse the data of each study is outlined. We then examine how random slope multilevel models have been used to determine the relationship between teacher and/or school effectiveness in terms of quality and equity. Implications of findings are drawn. In the final part of the chapter, we report the results of secondary analyses of each PISA cycle, which help us explore the relationship between quality and equity at school and country levels. In each of these secondary analyses, the SES gap in student achievement is treated as an indicator when measuring equity in education. A value-added approach is used to measure quality when analysing the datasets of the national studies, whereas in the case of PISA we only look at final student learning outcomes. Despite this difference and the fact that different types of learning outcomes are considered, a relationship between quality and equity seems to exist at all levels: classroom, school, system. Implications of findings for the theoretical and methodological development of EER are drawn and suggestions for using this approach in analysing data of experimental studies are provided.
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References
Andrich, D. (1988). A general form of Rasch’s Extended Logistic Model for partial credit scoring. Applied Measurement in Education, 1(4), 363–378.
Caro, D. H., Sandoval-Hernández, A., & Lüdtke, O. (2014). Cultural, social, and economic capital constructs in international assessments: An evaluation using exploratory structural equation modeling. School Effectiveness and School Improvement, 25(3), 433–450.
Catts, H. W. (1997). The early identification of language-based impairments and reading disabilities. Language, Speech and Hearing Services in the Schools, 28(1), 86–89.
Chudgar, A., & Luschei, T. F. (2009). National income, income inequality, and the importance of schools: A hierarchical cross-national comparison. American Educational Research Journal, 46(3), 626–658.
Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education. London: Routledge/Falmer.
Creemers, B. P. M., & Kyriakides, L. (2008). The dynamics of educational effectiveness: a contribution to policy, practice and theory in contemporary schools. London/New York: Routledge.
Creemers, B. P. M., & Kyriakides, L. (2010). School factors explaining achievement on cognitive and affective outcomes: Establishing a dynamic model of educational effectiveness. Scandinavian Journal of Educational Research, 54(1), 263–294.
Creemers, B. P. M., & Kyriakides, L. (2015). Developing, testing and using theoretical models of educational effectiveness for promoting quality in education. School Effectiveness and School Improvement, 26(1), 102–119.
Doyle, A. (2008). Educational performance or educational inequality: What can we learn from PISA about France and England? Compare: A Journal of Comparative and International Education, 38(2), 205–217.
Geary, D. C. (1994). Children’s mathematical development: Research and practical applications. Washington, DC: American Psychological Association.
Ginsburg, H. P., Jacobs, S. F., & Lopez, L. S. (1998). The teacher’s guide to flexible interviewing in the classroom: Learning what children know about math. Boston: Allyn and Bacon.
Goldstein, H. (2003). Multilevel statistical models (3rd ed.). London: Edward Arnold.
Kelly, A. (2012). Measuring ‘equity’ and ‘equitability’ in school effectiveness research. British Educational Research Journal, 38(6), 977–1002.
Kreuter, F., Eckman, S., Maaz, K., & Waterman, R. (2010). Children’s reports of parents’ socio-economic status: Does it matter whom you ask and what you ask about? Survey Research Methods, 4(3), 127–138.
Kyriakides, L. (1999). Research on baseline assessment in mathematics at school entry. Assessment in Education: Principles, Policy and Practice, 6(3), 357–375.
Kyriakides, L. (2002). A research based model for the development of policy on baseline assessment. British Educational Research Journal, 28(6), 805–826.
Kyriakides, L. (2005). Extending the comprehensive model of educational effectiveness by an empirical investigation. School Effectiveness and School Improvement, 16(2), 103–152.
Kyriakides, L., & Creemers, B. P. M. (2008). A longitudinal study on the stability over time of school and teacher effects on student learning outcomes. Oxford Review of Education, 34(5), 521–545.
Kyriakides, L., & Creemers, B. P. M. (2009). The effects of teacher factors on different outcomes: Two studies testing the validity of the dynamic model. Effective Education, 1(1), 61–86.
Kyriakides, L., Creemers, B. P. M., Antoniou, P., Demetriou, D., & Charalambous, C. (2015). The impact of school policy and stakeholders’ actions on student learning: A longitudinal study. Learning and Instruction, 36, 113–124.
Kyriakides, L., & Kelly, K. (2003). The impact of engagement in large scale assessment on teacher professional development: The Emergent Literacy Baseline Assessment project. Journal of Research in Childhood Education, 18(1), 38–56.
Micklewright, J., & Schnepf, S. V. (2007). Inequality of learning in industrialized countries. In S. P. Jenkins & J. Micklewright (Eds.), Inequality and poverty re-examined (pp. 129–145). Oxford, UK: Oxford University Press.
OECD. (2005). PISA 2003 Technical report. Paris: PISA, OECD Publishing.
OECD. (2009). PISA 2006 Technical report. Paris: PISA, OECD Publishing.
Rasbash, J., Steele, F., Browne, W., & Prosser, B. (2005). A user’s guide to MLwiN version 2.0. Bristol: University of Bristol.
Rutkowski, D., & Rutkowski, L. (2013). Measuring socioeconomic background in PISA: One size might not fit all. Research in Comparative and International Education, 8(3), 259–278.
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.
Smith, S. S. (1997). Early childhood mathematics. Needham Heights, MA: Allyn & Bacon.
White, K. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461–481.
Whitehurst, G. J., & Lonigan, C. J. (1998). Child development and emergent literacy. Child Development, 69(3), 848–872.
Willms, J. D. (2003). Ten hypotheses about socioeconomic gradients and community differences in children’s developmental outcomes. Quebec, Canada: Applied Research Branch Strategic Policy Human Resources Development Canada.
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Kyriakides, L., Creemers, B., Charalambous, E. (2018). Investigating the Relationship Between Quality and Equity: Secondary Analyses of National and International Studies. In: Equity and Quality Dimensions in Educational Effectiveness. Policy Implications of Research in Education, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-72066-1_5
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