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Study of Regularized Generalized Structured Component Analysis to Overcome Multicollinearity in Component-Based SEM

Kajian Regularized Generalized Structured Component Analysis untuk Mengatasi Multikolinearitas pada SEM Berbasis Komponen

Jurnal Matematika, Statistika dan Komputasi · Vol. 20 No. 1 (2023) · pp. 281–293

DOI
10.20956/j.v20i1.28069
ISSN
2614-8811, 1858-1382
Published
2023-09-06
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of and indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.

Indonesian

Multikolinearitas merupakan salah satu masalah yang dapat terjadi pada analisis Structural Equation Modeling (SEM). Indikasi terjadinya multikolinearitas adalah tingginya korelasi antara variabel laten dan korelasi antara indicator yang membentuk konstruk laten. Multikolinearitas dapat menyebabkan interpretasi hasil analisis SEM menjadi tidak tepat. Pada penelitian ini menerapkan Regularized generalized structured component analysis (RGSCA) sebagai solusi mengatasi multikolinearitas pada SEM berbasis komponen. Tujuan penelitian adalah menerapkan RGSCA pada data kemiskinan Jawa Timur 2021 yang mengandung masalah multikolinearitas. Langkah pertama adalah melakukan analisis data menggunakan GSCA namun diperoleh bobot indicator dan tidak signifikan, serta tiga estimasi path koefisien tidak signifkan pada interval kepercayaan 95%. Nilai korelasi antara indicator sangat tinggi yang mengindikasikan terjadinya multikolinearitas. Selanjutnya, dilakukan analisis data menggunakan RGSCA dengan menerapkan parameter ridge yang meghasilkan kesalahan prediksi (CV) minimum. Diperoleh seluruh estimasi loading factor, bobot dan path koefisien signifikan pada interval kepercayaan 95%. Hasil estimasi path koefisien menunjukkan variabel pendidikan, kesehatan dan ekonomi berpengaruh signifikan terhadap kemiskinan, variabel kesehatan dan ekonomi berpengaruh signifikan terhadap pendidikan, serta variabel kesehatan juga berpengaruh signifikan terhadap ekonomi. Hasil evaluasi model keseluruhan menunjukkan nilai FIT sebesar 0,662 artinya model mampu menjelaskan sekitar 66,2% variasi dari data.

Keywords

Collaboration network of the authors

12 authors · 20 co-authorship links · 3 across different organisations

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

  • Bandung Islamic University
  • State University of Semarang
  • Indonesia University of Education
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Prida Arya SaputraAffiliation unknown1
Hardin HardinAffiliation unknown1
Fauziah RoshafaraBandung Islamic University1
Puri Indah LestariBandung Islamic University1
Salwa SalsabilaBandung Islamic University1
Renita MaharaniBandung Islamic University1
Sri KadarwatiState University of Semarang1
SRI WAHYUNIAffiliation unknown1
Novi Ratna DewiState University of Semarang1
Lina HerlinaAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Study of Regularized Generalized Structured Component Analysis to Overcome Multicollinearity in Component-Based SEM</dc:title>
	<dc:title xml:lang="id">Kajian Regularized Generalized Structured Component Analysis untuk Mengatasi Multikolinearitas pada SEM Berbasis Komponen</dc:title>
	<dc:creator xml:lang="en">Amanah, Fitri </dc:creator>
	<dc:creator xml:lang="en">Rahmawati, Fitri </dc:creator>
	<dc:subject xml:lang="en">Regularized GSCA</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">SEM</dc:subject>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:description xml:lang="en">Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of  and  indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the  indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely   which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.</dc:description>
	<dc:description xml:lang="id">Multikolinearitas merupakan salah satu masalah yang dapat terjadi pada analisis Structural Equation Modeling (SEM). Indikasi terjadinya multikolinearitas adalah tingginya korelasi antara variabel laten dan korelasi antara indicator yang membentuk konstruk laten. Multikolinearitas dapat menyebabkan interpretasi hasil analisis SEM menjadi tidak tepat. Pada penelitian ini menerapkan Regularized generalized structured component analysis (RGSCA) sebagai solusi mengatasi multikolinearitas pada SEM berbasis komponen. Tujuan penelitian adalah menerapkan RGSCA pada data kemiskinan Jawa Timur 2021 yang mengandung masalah multikolinearitas. Langkah pertama adalah melakukan analisis data menggunakan GSCA namun diperoleh bobot indicator &amp;nbsp;dan &amp;nbsp;tidak signifikan, serta tiga estimasi path koefisien tidak signifkan pada interval kepercayaan 95%. Nilai korelasi antara indicator &amp;nbsp;sangat tinggi yang mengindikasikan terjadinya multikolinearitas. Selanjutnya, dilakukan analisis data menggunakan RGSCA dengan menerapkan parameter ridge &amp;nbsp;yang meghasilkan kesalahan prediksi (CV) minimum. Diperoleh seluruh estimasi loading factor, bobot dan path koefisien signifikan pada interval kepercayaan 95%. Hasil estimasi path koefisien menunjukkan variabel pendidikan, kesehatan dan ekonomi berpengaruh signifikan terhadap kemiskinan, variabel kesehatan dan ekonomi berpengaruh signifikan terhadap pendidikan, serta variabel kesehatan juga berpengaruh signifikan terhadap ekonomi. Hasil evaluasi model keseluruhan menunjukkan nilai FIT sebesar 0,662 artinya model mampu menjelaskan sekitar 66,2% variasi dari data.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2023-09-06</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/28069</dc:identifier>
	<dc:identifier>10.20956/j.v20i1.28069</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 20 No. 1 (2023): SEPTEMBER, 2023; 281- 293</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/28069/10284</dc:relation>
	<dc:relation>10.20956/j.v20i1.28069.g10284</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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