Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis

Budi Susetyo(1,Mail), Anwar Fitrianto(2)

(1) IPB University, Indonesia
(2) IPB University, Indonesia

MailCorresponding Author

Copyright (c) 2025 Budi Susetyo, Anwar Fitrianto
Article Metrics→
  
Indexing Database→



Download Full Text: PDF

Abstract

Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis. Hierarchical elements or higher levels often influence school accreditation and the national exam because education units are nested in the characteristics of the province. Objectives: This study aims to evaluate the relationship between accreditation and the national exam at the level of Junior high school/Madrasa in Java which are nested in province. Methods: The analysis employs multilevel GSCA analysis (MGSCA). Findings: UNBK has good convergent validity and it can explain each of the subjects tested in each province up to more than 90%. Concerning the estimates of path coefficients,  the study found eight patterns of relationship between SNP and UNBK that have a significant effect in the six provinces. Conclusion: The relationship between content and competency standard for UNBK shows that there are significant differences in all provinces in Java island. This shows that provincial characteristics affect school quality. The model can explain the total variability of all variables is 72.44%.

 

Keywords: multilevel generalized structured component analysis, national education standards, national examination.



DOI: http://dx.doi.org/10.23960/jpp.v12.i1.202223

References

Astivia, O. L. O., & Zumbo, B. D. (2019). Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS. Practical Assessment, Research, and Evaluation, 24(1), 1.

Audigier, V., White, I. R., Jolani, S., Debray, T. P. A., Quartagno, M., Carpenter, J., van Buuren, S., & Resche-Rigon, M. (2018). Multiple imputation for multilevel data with continuous and binary variables. Statistical Science, 33(2), 160–183.

Crockett, S. A. (2012). A five-step guide to conducting SEM analysis in counseling research. Counseling Outcome Research and Evaluation, 3(1), 30–47.

Hair, J. F. (2021). Reflections on SEM: An Introspective, Idiosyncratic Journey to Composite-based Structural Equation Modeling. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52(SI), 101–113.

Han, S., Capraro, R., & Capraro, M. M. (2015). How science, technology, engineering, and mathematics (STEM) project-based learning (PBL) affects high, middle, and low achievers differently: The impact of student factors on achievement. International Journal of Science and Mathematics Education, 13(5), 1089–1113.

Handayani, M. (2018). Utilization of national exam results in advancing educational ecosystem to improve the quality of education. ICEAP 2018, 1(1), 21–30.

Henseler, J. (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46(1), 178–192.

Hijrah, M., Susetyo, B., & Sartono, B. (2018). Structural equation modeling of national standard education of vocational high school using partial least square path modeling. International Journal of Scientific Research in Science Engineering and Technology, 4(4), 1418–1422. https://doi.org/10.32628/IJSRSET1844465

Hwang, H., Sarstedt, M., Cheah, J. H., & Ringle, C. M. (2020). A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA. Behaviormetrika, 47(1), 219–241.

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

Hwang, H., Takane, Y., & Malhotra, N. (2007). Multilevel generalized structured component analysis. Behaviormetrika, 34(2), 95–109.

Leckie, G., French, R., Charlton, C., & Browne, W. (2014). Modeling heterogeneous variance–covariance components in two-level models. Journal of Educational and Behavioral Statistics, 39(5), 307–332.

Moore, Z., Harrison, D. E., & Hair, J. (2021). Data Quality Assurance Begins Before Data Collection and Never Ends: What Marketing Researchers Absolutely Need to Remember. International Journal of Market Research, 63(6), 693–714.

Purwanto, A., & Sudargini, Y. (2021). Partial Least Squares Structural Squation Modeling (PLS-SEM) Analysis for Social and Management Research: A Literature Review. Journal of Industrial Engineering & Management Research, 2(4), 114–123.

Ryoo, J. H., & Hwang, H. (2017). Model evaluation in generalized structured component analysis using confirmatory tetrad analysis. Frontiers in Psychology, 8, 916.

Ryoo, J. H., Park, S., Kim, S., & Ryoo, H. S. (2020). Efficiency of cluster validity indexes in fuzzy clusterwise generalized structured component analysis. Symmetry, 12(9), 1514.

Setiawan, A. I., Susetyo, B., & Fitrianto, A. (2018). Application of generalized structural component analysis to identify relation between accreditation and national assessment. International Journal of Scientific Research in Science Engineering and Technology, 4(10), 93–97. https://doi.org/10.32628/18410IJSRSET

Suk, H. W., & Hwang, H. (2016). Functional generalized structured component analysis. Psychometrika, 81(4), 940–968.

Susetyo, B., & Wahyuni, R. (2021). Application of the fuzzy clusterwise generalized structured component method to evaluate implementation of national education standard in Indonesia. Management Science Letters, 11(4), 1379–1384. https://doi.org/10.5267/j.msl.2020.11.002

Vita, F., Susetyo, B., & Indriyanto, B. (2015). Generalized structured component analysis for national education standards of secondary school in Indonesia. Global Journal of Pure and Applied Mathematics, 11(4), 2441–2449.

Wu, W., Carroll, I. A., & Chen, P.-Y. (2018). A single-level random-effects cross-lagged panel model for longitudinal mediation analysis. Behavior Research Methods, 50(5), 2111–2124.

Zhu, C., Lopez, R. A., & Liu, X. (2016). Information cost and consumer choices of healthy foods. American Journal of Agricultural Economics, 98(1), 41–53.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


View My Stats