MULTIVARIATE MULTILEVEL MODELLING TO ASSESS FACTORS AFFECTING THE QUALITY OF VOCATIONAL HIGH SCHOOLS IN SOUTH SULAWESI PROVINCE

  • Abdullah Pannu Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
  • Hari Wijayanto Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
  • Budi Susetyo Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
Keywords: hierarchy, random effect, multivariate multilevel, PCA

Abstract

This study analyzes the quality of Vocational High Schools (VHS), which have a hierarchical data structure and have more than one response variable. Data gathered for this study is from the Basic Education Data (DAPODIK) in the form of raw data variables of several variables that characterize the quality of VHS and other independent variables in South Sulawesi for four years (2018 to 2021) from the Ministry of Finance Republic of Indonesia (KEMENKEU), and Statistics Indonesia (BPS). The explanatory variable at the regency level consists of four years (2018 to 2021), a multi-year and high-dimensional data structure. Therefore, Principal Component Analysis (PCA) is used to overcome this. The modelling is done by using multivariate multilevel modelling (MVMM) on one-level and two-level structures. This study aims to model the average National Examination and Accreditation scores of Vocational High School in South Sulawesi using MVMM modelling that considers the regency/city level and identifies the factors that influence the average National Examination and Accreditation scores. The results showed that the two-level multivariate model with a random intercept as a hierarchical component was better than the one-level multilevel model based on a minor Deviance Information Criterion (DIC) value. Simultaneously, at the 5% level of significance, variables that contribute significantly to the quality of Vocational High Schools in South Sulawesi Province are produced. The variables that have a significant effect on the quality of Vocational High Schools at the school level are the ratio of the number of students/pupils per study group, the percentage of certified teachers to the number of teachers, the ratio of the number of students/pupils per number of toilets, the ratio of laboratory availability, and the ratio of the availability of supporting rooms. Meanwhile, at the regency level, it was found that the percentage of poverty and Gross Regional Domestic Product (GRDP) had a significant effect on the quality of Vocational High Schools.

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References

Tom, A.B.S; Roel J.S, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling, 2nd Edition, London: SAGE Publications, 2012.

J. Gill and J. A. Wommack , The Multilevel Model Framework, United Kingdom (UK): SAGE Publications Ltd, 2016.

Hox J J; Moerbeek M; de Schoot R V, Multilevel Analysis Technique and Applications Third Edition, New York: Taylor & Francis, 2017.

H. Aguinis and S. A. Culpepper, "An Expanded Decision Making Procedure for Examining Cross-Level Interaction Effects with Multilevel Modeling," Organizational Research Methods, vol. 18, no. 2, pp. 155-176, 19 January 2015.

Snijders, TAB; Bosker, RJ, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling Second Edition, Los Angeles: SAGE, 2012.

Tabachnick, B. G.; Fidell, L. S, Using Multivariate Statistics (6th ed.), Boston, MA: Pearson, 2013.

Grilli L; Pennoni F; Rampichini C; Romeo I, "Exploiting TIMSS and PIRLS Combined Data: Multivariate Multilevel Modelling of Student Achievement.," The Annals of Applied Statistics, vol. Vol 10, no. No. 4, pp. 2405-2406, 2016.

Rachmat H.,Susetyo B., Indahwati., "Applied Multivariate Multilevel Modelling for Student Cognitive Achievement Analysis in AKSI 2019 Survey," IJSBAR, vol. Vol. 55, no. No. 1, pp. ISSN2307-4531, 2021.

Kementerian Pendidikan dan Kebudayaan, Pemenuhan Standar Nasional Pendidikan dan Mutu Satuan Pendidikan., Jakarta: Pusat Penelitian Kebijakan Pendidikan dan Kebudayaan, Badan Penelitian dan Pengembangan, Kementerian Pendidikan dan Kebudayaan, 2018.

Goldstein H, Multilevel Statistical Model Internet Edition, London: Arnold Publishers, 1999.

J. J. Hox and L. . M. Wijngaards-de, The multilevel regression model. In H. Best & C. Wolf (eds), TheSAGE handbook of regression analysis and causal inference., Thousand Oaks,CA: Sage, 2014.

Khurniawan AW, Erda G, "Potret Tenaga Kerja Lulusan SMK pada Industri Manufaktur.," Vocational Education Policy, vol. 1, no. 8, 2019.

Khurniawan, A. W. Sailah, I. Muliono, P. Maarif, M. S., Indriyanto, B., "Analysis of the Effect of School Governance and Total Quality Management on the Effectiveness of Vocational School-based Entreprise.," International Journal of Management (IJM), vol. Volume 11, no. Issue 9, pp. pp. 297-306, September 2020.

Badan Pusat Statistik, Statistik Indonesia 2021, Jakarta: BPS, 2021.

Badan Pusat Statistik Provinsi Sulawesi Selatan, Indikator Kesejahteraan Rakyat Sulawesi Selatan 2021, Makassar: BPS, 2021.

Mukhlason A; Winanti T; Yundra E, "Analisa Indikator SMK Penyumbang Pengangguran di Provinsi Jawa Timur," vol. Vol 02, no. No 02, pp. p29-36, 2020..

M. Turčičová, J. Mandel and K. Eben, "Multilevel maximum likelihood estimation with application to covariance matrices," Communications in Statistics - Theory and Methods, vol. 48, no. 4, pp. 909-925, 23 Jan 2018.

Hox J. J. , Multilevel Analysis Techniques and Applications : Quantitative Methodology Series 2nd Edition, New York: Routledge, 2010.

Badan Akreditasi Nasional Sekolah/Madrasah, Perangkat Akreditasi SMA/Sederajad, Jakarta (ID): Badan Akreditasi Nasional Sekolah/ Madrasah, 2021.

N. Shrestha, "Detecting Multicollinearity in Regression Analysis," American Journal of Applied Mathematics and Statistics, vol. 8, no. 2, pp. 39-42, 2020.

C. Deeming and J. Kelvyn, "Social Policy and Economic Development in Europe: Investigating the Macro Determinants of Individual Health and Well-being in a Multilevel Multivariate Analysis of Thirty European Nations," International Journal of Sociology, vol. 45, no. 4, 2013.

D. Liljequist, B. Elfving and K. S. Roaldsen, "Intraclass correlation – A discussion and demonstration of basic features," 22 July 2019.

Sorra, J; Dyer, N, "Multilevel Psychometric Properties of the AHRQ Hospital Survey on Patient Safety Culture," BMC Health Service Research, Vols. Vol 10,199, pp. 3-13, 2010.

T. K. Koo and Y. M. Li, "A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research," Journal of Chiropractic Medicine, vol. 15, no. 2, pp. 155-163, 2016.

Pramana S, Yuniarto B, Mariyah S, Santoso I, Nooraeni R, Data Mining dengan R: Konsep Serta Implementasi, Jakarta (ID): IN MEDIA-Anggota IKAPI No.250/JBA/2014, 2018.

Johnson RA.,Wichern DW., Applied Multivariate Statistical Analysis, Sixth Edition, New Jersey:: Pearson Prentice Hall, 2007.

S. N. Perdana, M. Handayani and J. Purnama, Analisis Hubungan Jumlah Rombongan Belajar dan Jumlah Peserta Didik per Rombongan Belajar Dengan Mutu Lulusan, Jakarta: Pusat Penelitian Kebijakan, Badan Penelitian dan Pengembangan dan, 2020.

. P. Zulvia, A. Kurnia and M. A. Soleh , "Multilevel modelling and panel data analysis in educational research (Case study: National examination data senior high school in West Java)," in Cite as: AIP Conference Proceedings, 2017.

Nurmayanti W.P, Notodiputro K.A, Indahwati, "Multilevel Modeling of Indonesian Student Achievement in Mathematics Based on TIMSS Data," International Journal of Scientific & Engineering Research, vol. 8, no. 2, February 2017.

Sayuti M., Mujiarto, "Employability skills in vocational high school context: An analysis of the KTSP curriculum," Journal of Vocational Education Studies, vol. 1, no. 2, pp. 33-44. , 28 11 2018.

Boer, L.F; Wijayanto, H; Indahwati, I, "Multilevel Modelling with Eigenvector Spatial Filtering and its Application to UN Score Data in Kendari.," International Journal of Sciences: Basic and Applied Research (IJSBAR)., vol. Vol 38, no. No.2, pp. 24-33, 03 May 2018..

Published
2022-12-15
How to Cite
[1]
A. Pannu, H. Wijayanto, and B. Susetyo, “MULTIVARIATE MULTILEVEL MODELLING TO ASSESS FACTORS AFFECTING THE QUALITY OF VOCATIONAL HIGH SCHOOLS IN SOUTH SULAWESI PROVINCE”, BAREKENG: J. Math. & App., vol. 16, no. 4, pp. 1515-1526, Dec. 2022.