The Multivariate Generalized Linear Model applies a lot in data science. However, this model is still less discussed in education, specifically in data analysis in the Program for International Student Assessment (PISA), which has complex structure data. This study aims to analyze the factors that influence the PISA's scores of Indonesian students simultaneously covering the three subjects of the PISA assessment, namely mathematics literacy, science literacy, and reading literacy. The complexity of PISA data which involves multivariate response variables which assumes a correlation between response variables, adds to the complexity of the analysis. One approach is the Multivariate Generalized Linear Model with the Quasi Likelihood estimation method. Took the data sources from the PISA survey was conducted by Organization for Economic Cooperation and Development in 2018. This study indicates that the factors that influence the PISA's scores of Indonesian students simultaneously are the class taken, parental education, facilities at home, student discipline, teacher feedback during learning, age of entering kindergarten, and failing a grade during elementary school. Based on the model diagnostic, it can conclude that Multivariate Generalized Linear Model produces a model that fits in modeling the PISA's scores of Indonesian students.

1.
I.
Pratiwi
,
Efek Program Pisa Terhadap Kurikulum di Indonesia
,
Jurnal Pendidikan dan Kebudayaan
, (
2019
).
2.
OECD
,
Sampling in PISA Indonesia
(March
2016
).
3.
J. D.
Willms
,
In Conversation Student Engagement: A Leadership Priority
II
(
2
), (
2011
), pp.
1
12
, .
4.
J.
Ozga
,
Assessing PISA European Educational Research Journal
11
(
2
), (
2012
), pp.
166
171
, .
5.
R.
Pakpahan
,
Faktor-Faktor Yang Memengaruhi Capaian Literasi Matematika Siswa Indonesia dalam Pisa 2012
Jurnal Pendidikan Dan Kebudayaan
,
1
(
3
), (
2016
), pp.
331
, .
6.
V. M.
Santi
,
K. A.
Notodiputro
, and
B.
Sartono
,
Variable selection methods applied to the mathematics scores of Indonesian students based on convex penalized likelihood
Journal of Physic Prosiding Series
1402
(
7
), (
2019
), .
7.
Julian J.
Faraway
,
Extending the Linear Model with R Generalized Linear, Mixed Effects and Nonparametric Regression Models
(
Taylor & Francis
,
New York
,
2016
).
8.
C.
McCulloch
and
S.
Searle
,
Generalized, Linear, and Mixed Models Generalized, Linear, and Mixed Models
(
John Wiley & Sons
,
New York
,
2001
).
9.
V. M.
Santi
,
A.
Kurnia
, and
K.
Sadik
,
Modelling of the number of malarias suffers in Indonesia using Bayesian generalized linear models
Journal of Physic Prosiding Series
1402
(
7
), (
2019
), .
10.
P.
McCullagh
and
J. A.
Nelder
,
Generalized Linear Models, Second Edition
(
Chapman and Hall
,
London
,
1989
).
11.
A.
Agresti
,
Categorical Data Analysis
, 2nd ed (
John Wiley & Sons
,
Canada
,
2002
).
12.
A. J.
Dobson
,
An Introduction to Generalized Linear Models
(
Chapman and Hall
,
New York
,
2002
).
13.
Jamilatuzzahro,
R.
Herliansyah
, and
R. E.
Caraka
,
Aplikasi generalized linear model pada R
(
Innosain
,
Yogyakarta
,
2018
).
14.
L.
Fahrmeir
and
G.
Tutz
,
Multivariate Statistical Modelling Based on Generalized Linear Models
(
Springer-Verlag
,
New York
,
1994
).
15.
W. H.
Bonat
and
B.
Jørgensen
,
Multivariate covariance generalized linear models Journal of the Royal Statistical Society
,
Series C: Applied Statistics
,
65
(
5
),
649
675
, (
2016
). .
16.
W. H.
Bonat
,
Multiple response variables regression models in R: The mcglm package
Journal of Statistical Software
,
84
(
4
),
2018
. .
17.
B.
Jørgensen
and
S. J.
Knudsen
,
Parameter Orthogonality and Bias Adjustment for Estimating Functions Scandinavian
Journal of Statistics
,
31
(
1
),
93
114
,
2004
. .
18.
R. A.
Johnson
and
D. W.
Wichern
,
Applied Multivariate Statistical Analysis
(
Prentice Hall
,
New jersey
,
2007
).
19.
N. S.
Yusoff
,
M. A.
Djauhari
, and
S.
Shariff
,
Power of test: Box M's test versus Jennrich's test
,
2015
.
21.
K.
Saputri
, Fauzi, and Nurhaidah,
Faktor-Faktor yang Mempengaruhi Literasi Anak Kelas 1 SD Negeri 20 Banda Aceh
,
Jurnal Ilmiah Pendidikan Guru Sekolah Dasar
,
2
(
1
),
98
104
,
2017
. Retrieved from jim.unsyiah.ac.id/pgsd/article/view/2537.
22.
E.
Palenewen
and
Y.
Alviolita
,
Pengaruh Penggunaan Software Pembelajaran Multimedia AI-LEARN Terhadap Hasil Belajar Biologi Siswa Kelas VIII SMP Negeri 9
Samarinda Tahun Pembelajaran 2013/2014
,
2014
.
23.
D. S.
Chrisman
and
H.
Pramusinto
,
Pengaruh Disiplin Belajar, Lingkungan Sekolah
,
Dan Fasilitas Belajar Terhadap Hasil Belajar Economic Education Analysis Journal
,
7
(
1
),
279
285
,
2018
.
24.
E.
Sulasmi
,
Analisis Faktor-Faktor Yang Mempengaruhi Prestasi Belajar Ditinjau dari Aspek Manajemen Minat Belajar Siswa Jurnal Manajemen Pendidikan Dasar
,
Menengah Dan Tinggi (JMP-DMT)
,
1
(
1
),
10
17
,
2020
. Retrieved from http://jurnal.umsu.ac.id/index.php/JMP-DMT/article/view/3920.
25.
S. E.
Wahyuni
,
M.
Tendri
, and
N. I.
Kusumawati
,
Hubungan Gaya Belajar dengan Prestasi Belajar Matematika Siswa Kelas XI SMK Muhammadiyah 1 Palembang Angewandte Chemie
International Edition
,
6
(
11
),
951
952
, 3(2), 208–216,
2021
.
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