Advertisement

Compositional Effects in Italian Primary Schools: An Exploratory Analysis of INVALSI SNV Data and Suggestions for Further Research

  • Marco Petracco-Giudici
  • Daniele Vidoni
  • Rossana Rosati
Part of the Communications in Computer and Information Science book series (CCIS, volume 112)

Abstract

The EU2020 strategy, which aims at turning “the EU into a smart, sustainable and inclusive economy delivering high levels of employment, productivity and social cohesion”, heavily relies on the human capital of its citizens. As a solid strand of literature posits, formal education is crucial for the development of individual human capital (among others: Barro & Lee 2001; Hanushek & Kimko 2000; Hanushek & Woessmann 2007; 2010). Indeed, one of the 5 headline targets of the strategy attains to reducing the share of early school leavers to less than 10% and ensuring that at least 40% of the younger generation reaches a tertiary degree.

Keywords

Contextual Effect Compositional Effect School Attainment Early School Leaver School Composition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ammermueller, A., Pischke, J.: Peer Effects in European Primary Schools: Evidence from PIRLS, Institute for the Study of Labor, IZA (2006)Google Scholar
  2. Barro, R.J., Lee, J.: International data on educational attainment: updates and implications. Oxford Economic Papers 53(3), 541–563 (2001)CrossRefGoogle Scholar
  3. Campodifiori, E., et al.: Un indicatore di status socio-economico-culturale degli allievi della quinta primaria in Italia. Istituto nazionale per la valutazione del sistema educativo di I-stru-zio-ne e di formazione (INVALSI), Rome (2010)Google Scholar
  4. Cipollone, P., Rosolia, A.: Social Interactions in High School: Lessons from an Earthquake. The American Economic Review 97(3), 948–965 (2007)CrossRefGoogle Scholar
  5. Coleman, J.S., et al.: Equality of Educational Opportunity. US Government Printing Office, Washington (1966)Google Scholar
  6. Coulombe, S., Tremblay, J., Marchand, S.: Literacy scores, human capital and growth across fourteen OECD countries. Statistics Canada, Ottawa (2004)Google Scholar
  7. Ebbes, P., Böckenholt, U., Wedel, M.: Regressor and random-effects dependencies in multilevel models. Statistica Neerlandica 58(2), 161–178 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  8. Edmonds, R.: Effective Schools for the Urban Poor. Educational Leadership 37(1), 15–18, 20–24 (1979)Google Scholar
  9. van Ewijk, R., Sleegers, P.: The effect of peer socioeconomic status on student achievement: A meta-analysis. Educational Research Review 5(2), 134–150 (2010)CrossRefGoogle Scholar
  10. Falorsi, S.: Nota metodologica sulla strategia di Campionamento. Istituto nazionale per la valutazione del sistema educativo di Istruzione e di formazione (INVALSI), Rome (2009)Google Scholar
  11. Fullan, M.: The Meaning of Educational Change. Teachers College Press, New York (1982)Google Scholar
  12. Gay, G.: Valutazione degli apprendimenti disciplinari nella scuola secondaria di primo grado. Istituto regionale di ricerca della Lombardia (IRER), Milan (2005)Google Scholar
  13. Gray, J., Goldstein, H., Jesson, D.: Changes and improvements in schools’ effectiveness: trends over five years. Research Papers in Education 11(1), 35 (1996)CrossRefGoogle Scholar
  14. Grilli, L., Rampichini, C.: Selection bias in random intercept models. Multilevel Modelling Newsletter 17(1), 9–18 (2005)Google Scholar
  15. Hanushek, E.A.: Conceptual and Empirical Issues in the Estimation of Educational Production Functions. The Journal of Human Resources 14(3), 351–388 (1979)CrossRefGoogle Scholar
  16. Hanushek, E.A.: Education and Race: An Analysis of the Educational Production Process. D.C. Heath & Co., Boston (1972)Google Scholar
  17. Hanushek, E.A., Kain, J.F., Markman, J.M.: Does peer ability affect student achievement? Journal of Applied Econometrics 18(5), 527–544 (2003)CrossRefGoogle Scholar
  18. Hanushek, E.A., Kimko, D.D.: Schooling, Labor-Force Quality, and the Growth of Nations. The American Economic Review 90(5), 1184–1208 (2000)CrossRefGoogle Scholar
  19. Hanushek, E.A., Woessmann, L.: The Economics of International Differences in Educational Achievement (2010)Google Scholar
  20. Hanushek, E.A., Woessmann, L.: The role of education quality for economic growth. The World Bank, Washington D.C (2007)CrossRefzbMATHGoogle Scholar
  21. Henderson, V., Mieszkowski, P., Sauvageau, Y.: Peer group effects and educational production functions. Journal of Public Economics 10(1), 97–106 (1978)CrossRefGoogle Scholar
  22. Hill, P.W., Rowe, K.J.: Modelling Student Progress in Studies of Educational Effectiveness. School Effectiveness and School Improvement: An International Journal of Research, Policy and Practice 9(3), 310 (1998)CrossRefGoogle Scholar
  23. Hoxby, C.M.: Peer effects in the classroom: learning from gender and race variation. NBER, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  24. INVALSI: Le competenze in scienze, lettura e matematica degli studenti quindicenni. In: Rapporto Nazionale PISA 2006, Armando editore, Roma (2008)Google Scholar
  25. INVALSI: Le prove del Servizio Nazionale di Valutazione 2008-2009 - Analisi Tecnica. Istituto nazionale per la valutazione del sistema educativo di Istruzione e di formazione (INVALSI), Rome (2010)Google Scholar
  26. INVALSI: Ricerca Internazionale IEA PIRLS 2006. Lettura nella scuola primaria. Rapporto nazionale. Armando editore, Rome (2008)Google Scholar
  27. INVALSI: Servizio Nazionale di Valutazione a.s. 2008-2009. Rilevazioni degli apprendimenti. Scuola primaria. Prime analisi. Istituto nazionale per la valutazione del sistema educativo di Istruzione e di formazione (INVALSI), Rome (2009)Google Scholar
  28. Kim, J., Frees, E.: Omitted Variables in Multilevel Models. Psychometrika 71(4), 659–690 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  29. Kramarz, F., Machin, S.J., Ouazad, A.: What Makes a Test Score? The Respective Contributions of Pupils, Schools, and Peers in Achievement in English Primary Education. SSRN eLibrary (2009)Google Scholar
  30. Krueger, A.B., Lindahl, M.: Education for Growth: Why and For Whom? Journal of Economic Literature 39(4), 1101–1136 (2001)CrossRefGoogle Scholar
  31. Manski, C.F.: Identification of Endogenous Social Effects: The Reflection Problem. The Review of Economic Studies 60(3), 531–542 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  32. McEwan, P.J.: Peer effects on student achievement: evidence from Chile. Economics of Education Review 22(2), 131–141 (2003)MathSciNetCrossRefGoogle Scholar
  33. Mortimore, P., et al.: School Matters: the Junior Years. Open Books, Wells (1988)Google Scholar
  34. Mullis, I.V.S., Martin, M.O., Foy, P.: TIMSS 2007 international mathematics report. TIMMS & PIRLS International Study Center, Boston College, Chestnut Hill (2008)Google Scholar
  35. OECD: Education at a Glance 2010 Revised edn., Organization for Economic Co-operation and Development (OECD) (2010) Google Scholar
  36. OECD: Towards better schools and more equal opportunities for learning. In: OECD Economic Surveys, Italy, ch. 4, pp. 93–130 (2009)Google Scholar
  37. OECD & JRC, Handbook on Constructing Composite Indicators: Methodology and User Guide, Paris, France (2008)Google Scholar
  38. Rabe-Hesketh, S., Skrondal, A.: Multilevel and Longitudinal Modeling Using Stata, 2nd edn. STATA press, College Station (2008)zbMATHGoogle Scholar
  39. Raudenbush, S.W., Willms, D.J.: The Estimation of School Effects. Journal of Educational and Behavioral Statistics, 307–335 (1995)Google Scholar
  40. Rutter, M., et al.: Fifteen Thousand Hours: Secondary Schools and Their Effects on Children. Harvard University Press, Cambridge (1982)Google Scholar
  41. Schlotter, M., Schwerdt, G., Woessmann, L.: Econometric Methods for Causal Evaluation of Education Policies and Practices: A Non-Technical Guide. SSRN eLibrary (2009)Google Scholar
  42. Sirin, S.R.: Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research 75(3), 417–453 (2005)CrossRefGoogle Scholar
  43. Snijders, T.A.B., Bosker, R.J.: Multilevel analysis: an introduction to basic and advanced multilevel modeling. Sage, Thousand Oaks (1999)zbMATHGoogle Scholar
  44. Snijders, T.A.B., Berkhof, J.: Diagnostic checks for multilevel models. In: de Leeuw, J., Meijer, E. (eds.) Handbook of Multilevel Analysis. Springer, New York (2008)Google Scholar
  45. Steele, F., Vignoles, A., Jenkins, A.: The effect of school resources on pupil attainment: a multilevel simultaneous equation modelling approach. Journal of the Royal Statistical Society: Series A (Statistics in Society) 170(3), 801–824 (2007)MathSciNetCrossRefGoogle Scholar
  46. Thrupp, M., Lauder, H., Robinson, T.: School composition and peer effects. International Journal of Educational Research 37(5), 483–504 (2002)CrossRefGoogle Scholar
  47. Willms, J.D.: Social Class Segregation and Its Relationship to Pupils’ Examination Results in Scotland. American Sociological Review 51(2), 224–241 (1986)CrossRefGoogle Scholar
  48. Witziers, B., Bosker, R.J., Krüger, M.L.: Educational Leadership and Student Achievement: The Elusive Search for an Association. Educational Administration Quarterly 39(3), 398–425 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marco Petracco-Giudici
    • 1
    • 2
  • Daniele Vidoni
    • 3
  • Rossana Rosati
    • 1
  1. 1.Institute for the Protection and Security of the Citizen, Econometrics and Applied Statistics UnitEuropean Commission, Joint Research CentreItaly
  2. 2.Joint Research CentreIspra (VA)Italy
  3. 3.Istituto nazionale per la valutazione del sistema educativo di istruzione e di formazione (INVALSI)Italy

Personalised recommendations