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Open access Jurnal SINTA 3

QUANTILE BASED PLS-SEM WITH WILD BOOTSTRAP

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 20 No. 2 (2026) · pp. 1775–1790

DOI
10.30598/barekengvol20iss2pp1775-1790
ISSN
2615-3017, 1978-7227
Published
2026-01-26
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Partial Least Squares SEM (PLS-SEM) is the recommended technique for structural equation modeling (SEM), which assesses correlations between latent components concurrently, particularly for small samples and non-normal data. But because traditional PLS-SEM only calculates average correlations between constructs, it runs the risk of overlooking variances in the quantile distribution. Consequently, the creation of the Quantile PLS-SEM approach, which incorporates quantile regression, provides a means to examine correlations across the entire data distribution. To improve estimation, wild bootstrap is used to address heteroscedasticity issues and produce more reliable inferences. The purpose of this study is to develop and apply Quantile based PLS-SEM with Wild Bootstrap to analyze the gizi data status of the Indonesian population based on the Survey Status Gizi Indonesia 2024. The analysis's findings indicate that specific and sensitive interventions have a significant impact on the gizi status of different quantities.

Collaboration network of the authors

23 authors · 58 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Politeknik Perkapalan Negeri Surabaya
  • Universitas Airlangga
  • Universitas Islam Negeri Maulana Malik Ibrahim
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
S P RahayuAffiliation unknown1
Abdullah EmbongInstitut Teknologi Sepuluh Nopember1
Jasni Mohammad ZainAffiliation unknown1
Diah Puspito WulandariInstitut Teknologi Sepuluh Nopember1
Arief KurniawanInstitut Teknologi Sepuluh Nopember1
Alvin NathanaelInstitut Teknologi Sepuluh Nopember1
Anda L. JunianiPoliteknik Perkapalan Negeri Surabaya1
Wardah Rahmatul IslamiyahUniversitas Airlangga1
Sri HariniUniversitas Islam Negeri Maulana Malik Ibrahim1
Cindy Cahyaning AstutiAffiliation unknown1
Angga Dwi MulyantoAffiliation unknown1
Hasri Wiji AqsariAffiliation unknown1
Darwis DarwisUniversitas Halu Oleo1
Iwan KurniawanAffiliation unknown1
Muhammad ZulfadhliAffiliation unknown1
Riwi Dyah PangestiAffiliation unknown1
Muhammad Idham KurniawanAffiliation unknown1
Albertus Eka Putra HaryantoAffiliation unknown1
Raupong RaupongAffiliation unknown1
M. Sjahid AkbarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">QUANTILE BASED PLS-SEM WITH WILD BOOTSTRAP</dc:title>
	<dc:creator>Balami, Abdul Malik</dc:creator>
	<dc:creator>Otok, Bambang Widjanarko</dc:creator>
	<dc:creator>Purnami, Santi Wulan</dc:creator>
	<dc:description xml:lang="en-US">Partial Least Squares SEM (PLS-SEM) is the recommended technique for structural equation modeling (SEM), which assesses correlations between latent components concurrently, particularly for small samples and non-normal data. But because traditional PLS-SEM only calculates average correlations between constructs, it runs the risk of overlooking variances in the quantile distribution. Consequently, the creation of the Quantile PLS-SEM approach, which incorporates quantile regression, provides a means to examine correlations across the entire data distribution. To improve estimation, wild bootstrap is used to address heteroscedasticity issues and produce more reliable inferences. The purpose of this study is to develop and apply Quantile based PLS-SEM with Wild Bootstrap to analyze the gizi data status of the Indonesian population based on the Survey Status Gizi Indonesia 2024. The analysis's findings indicate that specific and sensitive interventions have a significant impact on the gizi status of different quantities.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2026-01-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/20932</dc:identifier>
	<dc:identifier>10.30598/barekengvol20iss2pp1775-1790</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application; 1775-1790</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol20iss2year2026</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/20932/12473</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Abdul Malik Balami, Bambang Widjanarko Otok, Santi Wulan Purnami</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>