Correspondence analysis of time series data of results of gymnasium exams in Poland

• Authors: Agnieszka Stanimir
• Languages of publication: EN

Abstracts

EN

The aim of this article is to present the possibilities of using correspondence analysis in the study of changes in the gymnasium exam results in Poland. The study used exam results from 2003 and 2010 of the Regional Examination Board, which consist of two regions: Lower Silesia and Opolskie. The usefulness of correspondence analysis using multiway contingency tables and correspondence analysis of supplementary points is shown. From the examinations’ differences between the levels of exam results in both years, gender and place of exam are indicated.

Keywords

EN

correspondence analysis; multiway contingency tables; supplementary points; gymnasium exam

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2014
• Issue: 1(43)
• Pages: 39-49

Contributors

author

Agnieszka Stanimir

References

• Andersen E.B., The Statistical Analysis of Categorical Data, Springer-Verlag, Berlin 1991.
• Backhaus K., Erichson B., Plinke W., Weiber R., Multivariate Analysemethoden, Springer-Verlag, Berlin 2003.
• Blasius J., Korrespondenzanalyse, R. Oldenbourg Verlag, München 2001.
• Clausen S.E., Applied Correspondence Analysis. An Introduction, University Paper 121, Sage 1998.
• Dore J.C., Ojasoo T., How to analyze publication time trends by correspondence factor analysis: Analysis of publications by 48 countries in 18 disciplines over 12 years, “Journal of The American Society for Information Science and Technology“ 2001, no. 52(9), pp. 763-769.
• Eurydice: Educational System in Poland, https://webgate.ec.europa.eu/fpfis/mwikis/eurydice/index.php/Poland:Overview (accessed 10.07.2013).
• Greenacre M., Correspondence Analysis in Practice, Academic Press, London 1993.
• Greenacre M., Theory and Application of Correspondence Analysis, Academic Press, London 1984.
• Greenacre M., From Simple to Multiple Correspondence Analysis, [in:] Multiple Correspondence Analysis and Related Methods, ed. M. Greenacre, J. Blasius, Chapman&Hall/CRC, London 2006, pp. 41-76.
• Jobson J.D., Applied Multivariate Data Analysis. Vol. II: Categorical and Multivariate Methods, Springer-Verlag, New York 1992.
• Muller-Schneider T., The Visualisation of Structural Change by Means of Correspondence Analysis, [in:] Correspondence Analysis in the Social Sciences, ed. M. Greenacre, J. Blasius, Academic Press, 1994, pp. 267-280.
• Skala staninowa ,http://www.oke.wroc.pl/images/library/File/pdfy/SP_Staniny_2011.pdf (accessed 10.07.2013).
• Stanimir A., Analiza korespondencji jako narzędzie do badania zjawisk ekonomicznych, Wydawnictwo Akademii Ekonomicznej, Wrocław 2005.
• Thiessen V., Rohlinger H., Blasius J., The “Significance” of Minor Changes in Panel Data: a Correspondence Analysis of the Division of Household Tasks, [in:] Correspondence Analysis in the Social Sciences, ed. M. Greenacre, J. Blasius, Academic Press, 1994, pp. 267-280.
• Van der Heijden P.G.M., Mooijaart A., Takane Y., Correspondence Analysis and Contingency Table Models, [in:] Correspondence Analysis in the Social Sciences, ed. M. Greenacre, J. Blasius, Academic Press, 1994, pp. 79-111.
• Van der Heijden P.G.M., Teunissen J., Van Orlé C., Multiple correspondence analysis as a tool for quantification or classification of career data, “Journal of Educational and Behavioral Statistics”, December 21, 1997, no. 22, pp. 447-477.