Session Information
04 SES 05 B, Social Class
Paper Session
Contribution
One possible way to take into consideration the problem of inequalities within the European Educational Systems is to analyze the connection between background categories, such as social origins or gender and the students' level of proficiency [1]: the stronger the relationship, the higher the inequality. Another way to look at this issue is to evaluate which factors contribute to minimize the gap between the most and the least advantaged students [2].
Science proficiency levels in PISA are related to the various different competencies of students in science subjects [1]. The scores of students are divided into six proficiency levels, with Levels 5 and 6 at the top and Level 1 and below at the bottom (referring to students who do not possess even the most basic science skills and knowledge.).
Data is based on the answers of those European students, as well as those of principals who took part in the PISA 2006 study in Europe. The dependent variable of the analysis was a dichotomous variable the values of which represent the two different groups of students. The background variables were:
-socio-economic and cultural background (e.g. father and mother occupational status; home possessions; cultural possessions at home, parents' educational level);
-gender of the student;
-being native students or students from an immigrant background
-school mean values of student socio-economic and cultural variables, school type (e.g. private indipendent, public) and school community context (e.g. village, large city);
-country of the school.
The objective of this paper was to identify, using the European PISA 2006 data, the intersection of background categories which have the highest percentage of students performing at the lowest science proficiency levels compared to those students performing at the highest science proficiency levels. One should note that while it is quite usual to analyze the main effects of background variables on student performance [1], it is not so common to take into account the interaction effects, that is, the effect of the intersection of factors of inequality.
Method
Expected Outcomes
References
1. OECD (2007). PISA 2006 Science competencies for tomorrow’s world. Paris: OECD. 2. Levin, B. (2003). Approaches to equity in policy for lifelong learning. Paper prepared for the OECD, Paris. 3. Williams, C. J., Lee, S. S., Fisher, R. A.,&Dickerman, L. H. (1999). A comparison of statistical methods for prenatal screening for down syndrome. Applied Stochastic Models in Business and Industry, 15, 89-101. 4. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmount, CA: Wadsworth. 5. Allore, H., Tinetti, M. E., Araujo, K. L. B., Hardy, S., & Peduzzi, P. (2005). A case study found that a regression tree outperformed multiple linear regression in predicting the relationship between impairments and Social and Productive Activities scores. Journal of Clinical Epidemiology, 58, 154–161. 6. Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erbaum. 7. Fabbris, L. (1997). Statistica multivariata: analisi esplorativa dei dati. Milano: McGraw Hill.
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