Structural Causality Between National Examination Score and The School Accreditation Categories

Authors

Anwar Fitrianto , Budi Susetyo , Iswan Achlan Setiawan

DOI:

10.29303/jppipa.v8i1.1178

Published:

2022-01-06

Issue:

Vol. 8 No. 1 (2022): January

Keywords:

Structural equation modeling, Generalized structured component analysis, National education standards, National exams

Research Articles

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How to Cite

Fitrianto, A., Susetyo, B., & Achlan Setiawan, I. (2022). Structural Causality Between National Examination Score and The School Accreditation Categories. Jurnal Penelitian Pendidikan IPA, 8(1), 73–78. https://doi.org/10.29303/jppipa.v8i1.1178

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Abstract

This study aims to compare and determine the best model to describe the relationship between National Education Standard (NES) and CBNE scores using generalized structured component analysis. Model 1 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of National Education and the Ministry of Religion (2010), Model 2 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2012), and Model 3 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2017). The results of the structural model evaluation have found that in Model 1, the SI path coefficient to Academic Achievement (PA) is not significant, in Model 2, the SI path coefficient to PA and SPT to SPN is not significant and in Model 3, the SI path coefficient to PA is also not significant. The coefficient of determination of each endogenous latent variable for each model ranges from 0.20 - 0.75. While the resulting Q-square value for all models is more than 0.9 to represent very good predictive relevance. Based on the overall goodness of fit, it is found that Model 3 produces the largest FIT and AFIT values. So it can be said that model 3 is better than other models. This model produces 11 invalid indicator variables, namely points 17, 39, 51, 55, 57, 59, 73, 75, 76, 80, and 108. The study found that National Education Standards that significantly affect academic achievement are graduate competency standards, process standards, and educational assessment standards

References

Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York. Plenum Press.

Chin, W.W. (2010). How to write up and report PLS analyses. In Vinzi VE, Chin WW, Henseler J, Wang H. Handbook of Partial Least Squares: Concepts, Methods, and application. Vinzi VE, Chin WW, Henseler J, Wang H. Jerman (DE): 645-689. Springer. Retrieved from: https://link.springer.com/chapter/10.1007/978-3-540-32827-8_29

Ferezagia D.V. (2015). Model persamaan struktural delapan standar nasional pendidikan dengan generalized structured component analysis. (Unpublished Master Thesis). Institut Pertanian Bogor.

Ghozali I, Kusumadewi KA. (2016). Model persamaan struktural PLS-PM GSCA RGCCA. Semarang: Yoga Pratama.

Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297. https://doi.org/10.1207/s15328031us0304_4

Hijrah, M, et al., Susetyo B, Sartono B. 2018. Structural Equation Modeling of National Standard Education of Vocational High School Using Partial Least Square Path Modeling. IJSRSET. 4(4), 1418-1422. https://doi.org/10.32628/IJSRSET1844465

Hwang H, et al., (2012). Functional multi-set canonical correlation analysis. Psychometrika. 77(1), 48–64. https://doi.org/10.1007/S11336-011-9234-4

Hwang H, et al., (2013). A unified approach to multi-set canonical correlation analysis and principal component analysis: an application to functional neuroimaging data. Br. J. Math. Stat. Psychol. 66(1), 308-321. https://doi.org/10.1111/j.2044-8317.2012.02052.x

Hwang H., De Sarbo S. W., Takane Y. (2007). Fuzzy clusterwise generalized structured component analysis. Psychometrika 72(1), 181–198. https://doi.org/10.1007/s11336-005-1314-x

Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. US: Chapman and Hall/CRC.

Jung K, et al., (2012). Dynamics generalized structured component analysis with applications to the analysis of effective connectivity in functional neuroimaging data. Psychometrika. 77, 827–848. https://doi.org/10.1007/s11336-012-9284-2

Ministry of Education and Culture. 2017. Peraturan menteri pendidikan dan kebudayaan Nomor 003/H/AK/2017 tahun 2017 tentang kriteria dan perangkat akreditasi SMP/MTs. Jakarta. Sekretariat Negara.

Ministry of National Education and the Ministry of Religion. 2010. Sistem penjaminan mutu pendidikan: panduan teknis evaluasi diri sekolah (EDS). Jakarta. Direktorat Jenderal Pendidikan Dasar dan Menengah.

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001

Romdhani H, et al., (2015). Pathway-based association study of multiple candidate genes and multiple traits using structural equation models. Genet. Epidemiol. 39, 101–113. https://doi.org/10.1002/gepi.21872

Ryoo, J. H. & Hwang H. (2017). Model evaluation in generalized structured component analysis using confirmatory tetrad analysis. Frontiers in Psychology. 8, 1-10. https://doi.org/10.3389/fpsyg.2017.00916

Wold, H. (1982). Soft modeling: The basic design and some extensions. In systems under indirect observation: causality, structure, prediction. Amsterdam (NL): North Holland. https://doi.org/10.1207/s15328031us0304_4

Author Biographies

Anwar Fitrianto, Department of Statistics Faculty of Mathematics and Natural Sciences, IPB University

Research:

Scopus: https://www.scopus.com/authid/detail.uri?authorId=36179598200

Budi Susetyo, Department of Statistics, IPB University

Iswan Achlan Setiawan, Ministry of Education and Culture, Republic of Indonesia, Jakarta, Indonesia.

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