How Central is the PISA Outcomes on Human Development?


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Authors

  • Akif AVCU

DOI:

https://doi.org/10.33200/ijcer.851852

Keywords:

PISA, network modeling, development indicators

Abstract

Unlike the traditional statistical approaches, which imply the existence of a latent common causes that lead to the emergence and covariance of indicators, network modeling suggests that latent traits emerge due to interactions between indicators. Clearly, such kind of handling of the Program for International Student Assessment (PISA) results and other development indicators better reflect the mutual interactions of the indicators. By this aim, the network pattern of development indicators was revealed and graphically represented, most and least important indicators were detected. In addition, the indicators that have closer association with the PISA results were also detected. United Nations Development Program (UNDP) data were used for the analyzes. The 2015 year of data for sixty-six countries were used and consists of thirteen development indicators. The data were analyzed in R statistical program using “qgraph” package. The results showed that the PISA results are not at the central position compared to other development indicators while it was closely associated with gender inequality, secondary school completion rate and unequal life expectations. Those results were discussed based on the existing literature and some recommendations were given to policymakers and for future research.

Author Biography

Akif AVCU

Corresponding Author: Akif Avcu, avcuakif@gmail.com

Akif AVCU
Marmara University
0000-0003-1977-7592
Türkiye

References

• Afşar, M. (2009). Türkiye’de eğitim yatırımları ve ekonomik büyüme ilişkisi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 9(1), 85-98.

• Baldacci., E., Guin-Sui, M. T., & de Mello, L. (2004). More on the Effectiveness of Public Spending on Healthcare and Education: A Covariance Structure Model. Journal of International Development, 15(6), 709-725.

• Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747-3752. doi: 10.1073/pnas.0400087101

• Barro, R.J., (1997). The Determinants of Economic Growth. MIT Press, Cambridge, MA.

• Beard, C., Millner, A. J., Forgeard, M. J., Fried, E. I., Hsu, K. J., Treadway, M. T., ... & Björgvinsson, T. (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological medicine, 46(16), 3359-3369.

• Bloom, D. E., Canning, D., Chan, K. J., & Luca, D. L. (2014). Higher education and economic growth in Africa. International Journal of African Higher Education, 1(1), 22-57.

• Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5-13. doi: 10.1002/wps.20375

• Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. doi: 10.1146/annurev-clinpsy-050212-185608

• Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163-177. doi: 10.1080/0022250X.2001.9990249

• Chabbott, C. & Ramirez, F. O. (2000). Development and education. In Handbook of the Sociology of Education (pp. 163-187). Springer, Boston, MA.

• Chen, J. & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika. 95 (3), 759–771. doi: 10.1093/biomet/asn034

• Cinnirella, F., & Streb, J. (2017). The role of human capital and innovation in economic development: evidence from post-Malthusian Prussia. Journal of economic growth, 22(2), 193-227.

• Clark, D., & Royer, H. (2013). The effect of education on adult mortality and health: Evidence from Britain. American Economic Review, 103(6), 2087-2120.

• Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. J. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29. doi: 10.1016/j.jrp.2014.07.003

• Cramer, A. O. J., Waldorp, L., van der Maas, H., & Borsboom, D. (2010). Comorbidity: A Network Perspective. Behavioral and Brain Sciences, 33 (2-3), 137–150. doi: 10.1017/S0140525X09991567

• Cramer, A.O., Sluis, S.V., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S.H., Kendler, K.S., & Borsboom, D. (2012). Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't Like Parties if You Don't Like People: Dimensions of normal personality as networks. European Journal of Personality, 26(4), 414-431. doi: 10.1002/per.1866

• De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (2nd ed.). Cambridge: Cambridge University Press.

• Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195–212. doi: doi: 10.3758/s13428-017-0862-1

• Epskamp, S., Cramer, A.O.J., Waldorp, L.J. ,Schmittmann V. D. & Borsboom, D. (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48(4), 1-18. doi: 10.18637/jss.v048.i04

• Foygel B.R., & Drton, M. (2015). High-dimensional Ising model selection with bayesian information criteria. Electronic Journal of Statistics, 9 (1), 567–607. doi: : 10.1214/154957804100000000

• Foygel, B.R. & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Advances in Neural Information Processing Systems, 23, 2020-2028. doi: 10.5555/2997189.2997257

• Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. doi: 10.1016/0378-8733(78)90021-7

• Fried, E. I. (2015). Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Frontiers in Psychology, 6, 309. doi:10.3389/fpsyg.2015.00309

• Friedman, J. H., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441. doi: 10.1093/biostatistics/kxm045

• Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Software: Practice and experience, 21(11), 1129-1164. doi: 10.1002/spe.4380211102

• Grek, S. (2009). Governing by numbers: The PISA “effect” in Europe. Journal of Education Policy, 24(1), 23-37.

• Humphries, M. D., & Gurney, K. (2008). Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PloS one, 3(4), e0002051. doi: 10.1371/journal.pone.0002051

• Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16, 123–131. doi: 10.1.1.879.4893

• Kim, M., & Leskovec, J. (2012). Latent Multi-group Membership Graph Model. ArXiv, abs/1205.4546.

• Lauritzen, S. L. (1996). Graphical Models. Oxford Statistical Science Series. volume 17. New York, NY: Oxford University Press.

• Masuda N, Sakaki M, Ezaki T and Watanabe T (2018) Clustering Coefficients for Correlation Networks. Frontiers in Neuroinformatics. 12:7. doi: 10.3389/fninf.2018.00007

• McNally, R. J., Robinaugh, D. J., Wu, G. W. Y., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836–849. doi: 10.1177/2167702614553230

• Mcnally, R.J. (2016). Can network analysis transform psychopathology? Behaviour research and therapy, 86, 95-104. doi: 10.1016/j.brat.2016.06.006

• Meinshausen, N., & Bühlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. The annals of statistics, 34(3), 1436-1462. doi:10.1214/009053606000000281

• Newman, M. E. J. (2010). Networks: An introduction. New York, NY: Oxford University Press.

• OECD (2001). Knowledge and Skills for Life: First Results from PISA 2000. Paris: OECD Publishing.

• OECD (2006). Assessing scientific, reading and mathematical literacy: A framework for PISA 2006. Paris: OECD Publishing.

• OECD. (2010). Education at a glance 2010: OECD indicators. Paris: OECD.

• OECD. (2017). PISA 2015 assessment and analytical framework: science, reading, mathematic, financial literacy and collaborative problem solving (Revised Edition). Paris: PISA, OECD Publishing.

• OECD. (2018). PISA for development: results in focus. Paris: OECD.

• OECD. (2019). PISA 2018: Insights and interpretations. Paris: OECD publishing.

• Onnela, J. P., Saramaki, J., Kertesz, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71(6). doi: 10.1103/PhysRevE.71.065103

• Pongratz, L. A. (2006). Voluntary Self‐Control: Education reform as a governmental strategy. Educational Philosophy and Theory, 38(4), 471-482.

• R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

• Snijders T.A.B. (2009) Network Analysis, Longitudinal Methods of. In: Meyers R. (Eds.) Encyclopedia of Complexity and Systems Science. Springer, New York, NY.

• Takayama, K. (2009). Politics of externalization in reflexive times: Reinventing Japanese education reform discourses through “Finnish PISA success”. Comparative Education Review, 54(1), 51-75. doi: 10.1086/644838

• van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific reports, 4, 5918. doi: 10.1038/srep05918

• van Borkulo, C., Boschloo, L., Borsboom, D., Penninx, B. W., Waldorp, L. J., & Schoevers, R. A. (2015). Association of symptom network structure with the course of depression. JAMA psychiatry, 72(12), 1219-1226. doi:10.1001/jamapsychiatry.2015.2079

• Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature, 393(6684), 440-442. doi: 10.1038/30918

• Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1). doi: 10.2202/1544-6115.1128

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Published

2022-10-30

How to Cite

AVCU, A. (2022). How Central is the PISA Outcomes on Human Development?. International Journal of Contemporary Educational Research, 8(4), 16–26. https://doi.org/10.33200/ijcer.851852

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