Quality Index of School Education by Multiplicative aggregation

Authors

  • Satyendra Nath Chakrabartty Indian Ports Association, Indian Statistical Institute

DOI:

https://doi.org/10.56916/jirpe.v3i1.580

Keywords:

National Education Policy, School education, Overall performance, Multiplicative aggregation, Critical indicators

Abstract

Quality school education depends on a host of factors like tangible learning outcomes, efficient governance, necessary infrastructure, equitable academic opportunities, etc. For evaluation of evidence-based policy making in the education sector, the Ministry of Human Development and NITI Aayog, Govt. of India developed School Education Quality Index (SEQI) and assessed performance of States and Union Territories of India. The paper describes methodologically sound measure of overall performance in school education (OPSCI), avoiding normalization/scaling, selection of weights and covers all chosen indicators to reflect overall improvement/decline of a State/country in current year with respect to base year. The proposed index considering multiplicative aggregation of the chosen indicators has wider applications, satisfies desired properties, facilitates construction of OPSCI for India, in addition to State-wise indices. Thus, it is possible to have inter-country comparisons and inter-region comparisons. OPSCI also helps in identification of critical indicators requiring managerial attentions, plotting path of overall progress across time by a State or a country, testing statistical hypothesis regarding equality of the index for two countries/States or equality of the index for a single country/State across time using conventional t-tests on the logarithms of the observations. OPSCI with theoretical advantages is recommended. Future studies suggested.

References

Alf, EF and Grossberg, JM. (1979): The geometric mean: Confidence limits and significance tests, Perception &Psychophysics, 26 (5), 419-421

Arrow, K. J., & Raynaud, H. (1986): Social choice and multicriterion decision-making. Cambridge: MIT Press.

Arvidsson, R. (2019): On the use of ordinal scoring scales in social life cycle assessment, The Int. Jr. of Life Cycle Assessment, 24:604–606 doi.org/10.1007/s11367-018-1557-2

Chakrabartty, S. (2023). Item Deletions Based on Difficulty Values and Discriminating Values. Edukasiana: Jurnal Inovasi Pendidikan, 2(4), 285–293. https://doi.org/10.56916/ejip.v2i4.455

Chakrabartty, Satyendra (2023). Geometric mean approach to measure Composite Index. [RMd] Revista Multidisciplinar, Vol.6, No.1, 62 – 83. DOI: https://doi.org/10.23882/rmd.24160

Chakrabartty, Satyendra Nath (2020): Better use of Scales as Measuring Instruments in Mental Disorders. Journal of Neurology Research Review & Reports, Vol. 2(3); 1 – 7. https://doi.org/10.47363/ JNRRR/2020-(2)128

Chakravarty, S. R. (2003): A Generalized Human Development Index, Review of Development

Economics, 7(1), 99-114

Decancq, K. and Lugo, M. A. (2009): Weights in multidimensional indices of wellbeing: An overview. Econometric Reviews, 32(1), 7–34.

ElSarawy, M. M. (2016): Use of new technique to measure wellbeing index for North Africa countries in year 2012, Presented in the 34th IARIW General Conference Dresden, Germany, August 21-27

EPI Report (2016): Global Metrics for the Environment, http://www.epi.yale.edu

Greco, S, Ishizaka, A, Tasiou, M and Torrisi, G. (2019): On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness, Soc Indic Res 141:61–94 https://doi.org/10.1007/s11205-017-1832-9

Grupp, H., & Schubert, T. (2010): Review and new evidence on composite innovation indicators for evaluating national performance. Research Policy, 39(1), 67–78.

Kasparian, J, and Rolland, A.(2012): OECD’s Better life index: can any country be well ranked?, Journal of Applied Statistics, 39 (10),1-8, DOI: 10.1080/02664763.2012.706265

Koh D, Lee S, Kim H, et al (2018): Combining lead exposure measurements and experts’ opinion through a Bayesian framework Occupational and Environmental Medicine; 75:A120-A121.

Mhlanga, ST and Lall, M. (2022): Influence of Normalization Techniques on Multi-criteria

Decision-making Methods. Journal of Physics: Conference Series; Vol. 2224, doi:10.1088/1742-6596/2224/1/012076

Saisana,M., Saltelli, A. and Tarantola, S. (2005): Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators, Jr. of Royal Statistical Society Series A, 168(2),1-17

Segovia-González, MM and Contreras, I. (2023): A Composite Indicator to Compare the Performance of Male and Female Students in Educational Systems. Soc Indic Res 165, 181–212. https://doi.org/10.1007/s11205-022-03009-1

Seth, S. and Villar, A. (2017): Human development, inequality, and poverty: Empirical findings. OPHI Working Paper 111, University of Oxford.

Smith, P. (2002): Developing composite indicators for assessing health system efficiency, in Smith, P.C. (ed.) Measuring up: Improving the performance of health systems in OECD countries, OECD: Paris.

Tofallis, Chris (2014): Add or Multiply? A Tutorial on Ranking and Choosing with Multiple Criteria. INFORMS Transactions on Education 14(3):109-119. http://dx.doi.org/10.1287/ited.2013.0124

UNDP (2010): Human development report: The real wealth of nations: Pathways to human

Development. New York.

UNDP (2007): Fighting climate change: Human solidarity in a divided world. Human development report 2007/2008. New York: Palgrave Macmillan

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Published

2024-03-03

How to Cite

Chakrabartty, S. N. (2024). Quality Index of School Education by Multiplicative aggregation. Journal of Innovation and Research in Primary Education, 3(1), 11–20. https://doi.org/10.56916/jirpe.v3i1.580

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