MULTILEVEL REGRESSIONS FOR MODELING MEAN SCORES OF NATIONAL EXAMINATIONS
Abstract
National Exam known as UN score is the final evaluation to determine the achievement of national graduate competency standards in the school. The determinants of the achievement of the standards can’t be separated from the role of schools and local governments in which this regard is known as nested. In the field of statistics, this phenomenon can be described with a multilevel model, where level-1 is the school while level-2 is the district where the school is located. Several multilevel models are used to describe the phenomenon, the result shows that the two-level regression model without interaction is selected as the best model and the variables which affect the UN average scores significantly at level-1 are school status , the ratio between laboratories and students , while the variable at level-2 is expenditure per capita of district/city . From this study, that educational institutions' steps in achieving a graduation standard can be right on the target.
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References
N. R. Draper and H. Smith, Applied Regression Analysis. 1998.
R. Ula, R. Ibnas, K. Nurfadilah, and M. I. Nawawi, “Penerapan model regresi multilevel untuk data ketepatan waktu lulus mahasiswa (Application of Multilevel logistic regression to timeliness of graduation of students),” 2023. [Online]. Available: https://jurnal.unej.ac.id/index.php/MIMS/index
T. A. B. Snijder and R. J. Bosker, MULTILEVEL ANALYSIS An Introduction to Basic and Advanced Multilevel Modeling. 2012.
H. Goldstein, Multilevel Statistical Model, Fourth Edi. Wiley Series, 2011.
P. J. Austin, V. Goel, and C. van Walraven, “An Introduction to Multilevel Regression Models,” Canadian Journal of Public Health, vol. 92, no. 2, pp. 150–154, 2001.
F. Maulina Sari, “Pengaruh Sarana Prasarana dan Motivasi Belajar Terhadap Hasil Belajar Keterampilan Komputer dan Pengolahan Informasi (KKPI) Jurusan Teknik Audio Video,” VOLT Jurnal Ilmiah Pendidikan Teknik Elektro , vol. 1, no. 2, pp. 115–125, 2016.
W. Wandikbo and N. Suharto, “Jurnal Tata Kelola Pendidikan PENGARUH SARANA DAN PRASARANA PENDIDIKAN TERHADAP MOTIVASI BELAJAR SISWA DI SMP LABORATORIUM PERCONTOHAN UPI BANDUNG”, [Online]. Available: https://ejournal.upi.edu/index.php/jtkp
A. N. D. Cintya and J. Nugraha, “Pengaruh Sarana Prasarana dan Motivasi Belajar Terhadap Hasil Belajar Siswa Kelas XII Otomatisasi Tata Kelola Perkantoran di SMK Ketintang Surabaya,” 2021. [Online]. Available: https://journal.unesa.ac.id/index.php/jpap
D. Agnesia, M. N. Partha, and N. Subagio, “Hubungan Kompetensi Guru dengan Nilai Ujian Nasional di SMK Negeri 4 Samarinda Tahun Pembelajaran 2018/2019,” Jurnal Prospek: Pendidikan Ilmu Sosial dan Ekonomi, vol. 3, no. 1, pp. 1–11, 2021.
F. Aladejana and O. Aderibigbe, “Science laboratory environment and academic performance,” J Sci Educ Technol, vol. 16, no. 6, pp. 500–506, Dec. 2007, doi: 10.1007/s10956-007-9072-4.
R. Darwas, Syukhri, A. Wulandari, and A. Afthanorhan, “Level of student satisfaction with laboratory facilities using the importance performance analysis (IPA) method,” Journal of Advanced Research in Dynamical and Control Systems, vol. 12, no. 3, pp. 195–201, 2020, doi: 10.5373/JARDCS/V12I3/20201182.
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: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 4, pp. 1515–1526, Dec. 2022, doi: 10.30598/barekengvol16iss4pp1515-1526.
C. A. Bobak, P. J. Barr, and A. J. O’Malley, “Estimation of an inter-rater intra-class correlation coefficient that overcomes common assumption violations in the assessment of health measurement scales,” BMC Med Res Methodol, vol. 18, no. 1, Sep. 2018, doi: 10.1186/s12874-018-0550-6.
D. V Cicchetti, “Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology,” 1994.
T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J Chiropr Med, vol. 15, no. 2, pp. 155–163, Jun. 2016, doi: 10.1016/j.jcm.2016.02.012.
L. Pellis, N. L. W Franssen-van Hal, J. Burema, and J. Keijer, “The intraclass correlation coefficient applied for evaluation of data correction, labeling methods, and rectal biopsy sampling in DNA microarray experiments The intraclass correlation coefficient applied for evaluation of data correction, labeling methods, and rectal biopsy sam-pling in DNA microarray experiments,” Physiol Genomics, vol. 16, pp. 99–106, 2003, doi: 10.1152/physiolgenomics.
J. S. Sorra and N. Dyer, “Multilevel psychometric properties of the AHRQ hospital survey on patient safety culture,” 2010. [Online]. Available: http://www.biomedcentral.com/1472-6963/10/199
J. J. Hox, M. Moerbeek, and R. van De Schoot, Multilevel Analysis Techniques and Applications. 2018.
K. M. Ramachandran and C. P. Tsokos, Mathematical statistics with applications. Academic Press, 2009.
H. Rachmat and B. Susetyo, “Applied of Multivariate Multilevel Modeling for Student Cognitive Achievement Analysis in AKSI 2019 Survey,” International Journal of Sciences: Basic and Applied Research (IJSBAR) International Journal of Sciences: Basic and Applied Research, vol. 55, no. 1, pp. 190–200, 2021, [Online]. Available: http://gssrr.org/index.php?journal=JournalOfBasicAndApplied
J. W. Twisk, Applied Multilevel Analysis. Cambridge University Press, 2006.
L. V. Hedges and E. C. Hedberg, “Intraclass correlation values for planning group-randomized trials in education,” Educ Eval Policy Anal, vol. 29, no. 1, pp. 60–87, Mar. 2007, doi: 10.3102/0162373707299706.
J. Lorah, “Effect size measures for multilevel models: definition, interpretation, and TIMSS example,” Large Scale Assess Educ, vol. 6, no. 1, Dec. 2018, doi: 10.1186/s40536-018-0061-2.
V. O. Eduviere and P. E. Onojete, “Effect of School Quality Factors on Students Academic Performance in Social Studies in Delta State-Nigeria By,” 2014.
S. F. Karl Briones, R. R. Joy Dagamac, J. D. David, and C. B. Ann Landerio, “Factors Affecting the Students’ Scholastic Performance: A Survey Study,” Indonesian Journal of Educational Research and Technology, vol. 2, no. 1, pp. 97–102, 2021, doi: 10.17509/xxxx.xxxx.
S. Sirait, “Does Teacher Quality Affect Student Achievement? An Empirical Study in Indonesia,” Journal of Education and Practice, vol. 7, no. 27, pp. 34–41, 2016, [Online]. Available: www.iiste.org
I. G. Menezes et al., “Policy implications of achievement testing using multilevel models: The case of Brazilian elementary schools,” Front Psychol, vol. 7, no. NOV, Nov. 2016, doi: 10.3389/fpsyg.2016.01727.
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