Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca

Umi Narimawati; Jonathan Sarwono

doi:doi:10.11648/j.ajam.20241205.13

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Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca

Umi Narimawati

, Jonathan Sarwono^*

Published in American Journal of Applied Mathematics (Volume 12, Issue 5)

Received: 7 August 2024 Accepted: 2 September 2024 Published: 20 September 2024

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Abstract

The aim of the research is to review theories underlying the Structural Equation Modeling (SEM) procedure based on covariance (CBSEM), partial least square (PLSSEM) and component (GESCA SEM). The methods used are meta-analysis and systematic secondary data search. Results of the study are: First, theories underlying the CBSEM, PLSSEM and GESCA SEM procedures produce different characteristics in each SEM model. CBSEM models consist of two sub models, namely 1) Factor Analysis Model consisting of a) Exploratory Factor Analysis (EFA) which is designed for a situation where the relationship between indicators and latent variables is unknown or unclear; b) Confirmatory Factor Analysis (CFA) which is used for research where the researcher already has knowledge about the structure of the underlying latent variable (construct) and c) Full Latent Variable Model (LV). 2) PLSSEM consists of two sub model, namely reflective and formative models. GESCA SEM consists of structural / inner model and measurement / outer model. Second, the primary characteristics of CBSEM, PLSSEM and GESCA SEM are requirements of the amount of data sample; the sample data origin; and the software used to calculate the data due to the different statistical formulation, namely LISREL, SmartPLS and GSCA Pro Windows. Third, the main differences among the CBSEM, PLS SEM and GESCA SEM are in the uses of the unstandardized regression coefficients (b) versus the standardized regression coefficients (β). Thus, the researchers that are going to use those procedures must consider those three important findings.

Published in	American Journal of Applied Mathematics (Volume 12, Issue 5)
DOI	10.11648/j.ajam.20241205.13
Page(s)	133-140
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Structural Equation Modeling (SEM), CBSEM, PLSSEM, GESCA SEM

References

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[2]	Garson, David. (2016) Partial Least Square: Regression and Structural Equation Model. School of Public and International Affairs, North Carolina State University. www.statisticalassociates.com
[3]	Garson, D.G. 2006. Structural Equation Model. World Wide Web: http://hcl.chass.ncsu.edu/ssl/ssl.htm
[4]	Hair, J. F. Ringle, C. M & Sarstedt, M. (2011) PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, vol. 19, no. 2 (spring 2011), pp. 139–151. © 2011 M.E. Sharpe, In.
[5]	Hair, Jo & Alamer, Abdullah, (2022) Partial Least Squares Structural Equation Modelling (PLS-SEM) in Second Language and Education Research: Guidelines Using an Applied Example. PLS SEM in Second Language Research.
[6]	Hanafiah, M. Hafiz, (2020) Formative vs. Reflective Measurement Model: Guidelines for structural equation modeling research. International Journal of Analysis and Applications Volume 18, Number 5.
[7]	Hwang, Heungsun (2023) GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal. Routledge. Taylor & Francis Group.
[8]	Hwang, Heungsun,. (2013) Generalized Structured Component Analysis: A Component-based Approach to Structural Equation Modeling. Department of Psychology. McGill University.
[9]	Hwang, Heungsun, et, al., (2019) A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA. Behaviormetrika.
[10]	Henseler, J. Ringle, C. M. & Sinkovicks, R. R. (2009). The use of partial least square modeling in international marketing. New Challenges to International Marketing Advances in International Marketing, Volume 20, 277-319.
[11]	Hidayat, Rachmat & Wulandari, Patricia, (2022) Structural Equation Modelling (SEM) in Research: Narrative Literature Review. Open Access Journal of Social Sciences Volume 5, Issue 6.
[12]	Hoyle, R. H, (2012). Handbook of Structural Equation Modeling. London: The Guilford Press.
[13]	Hwang, H., et. al., (2017) Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error. Frontier in Psychology.
[14]	Jöreskog, K. G. & Sörbom, D. (2022). LISREL 11: Examples Guide. Chapel Hill, NC: Scientific Software International, Inc.
[15]	Loehlin, J. C & Beaujean, A. A, 2017. Latent Variable Models. New York: Routlegde Taylor & Francis Group.
[16]	Memon, Muntaz Ali, et. al. (2017) A Review of the Methodological Misconceptions and Guidelines related to the Application of Structural Equation Modeling: a Malaysian scenario. Journal of Applied Structural Equation Modeling. 1 (1) June 2017.
[17]	Narimawati, Umi & Sarwono, Jonathan, (2020) Ragam Analisis. Yogyakarta: Penerbit Andi.
[18]	Narimawati, Umi & Sarwono, Jonathan, (2023) PLS SEM untuk Riset Skripsi, Tesis dan Disertasi. Yogyakarta: Penerbit Andi.
[19]	Narimawati, Umi & Sarwono, Jonathan, (2023) Mengenal Pemodelan Persamaan Struktural dengan LISREL. Yogyakarta: Penerbit Gava Media & Penerbit Graha Ilmu.
[20]	Purwanto, Agus., et. al, (2021) Education Research Quantitative Analysis for Little Respondents: Comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Jurnal Studi Guru dan Pembelajaran, Vol. 4, No. 2, August.
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[22]	Schumacker, R. E. & Lomax, R. G, 2010. A Beginner’s Guide to Structural Equation Modeling. New York: Taylor & Francis Group.
[23]	Schumacker, R. E & Whitettaker, T. A. 2022. A Beginner Guide to Structural Equation Modeling Edisi 5. Routledge Taylor & Francis.

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APA Style

Narimawati, U., Sarwono, J. (2024). Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. American Journal of Applied Mathematics, 12(5), 133-140. https://doi.org/10.11648/j.ajam.20241205.13

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ACS Style

Narimawati, U.; Sarwono, J. Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. Am. J. Appl. Math. 2024, 12(5), 133-140. doi: 10.11648/j.ajam.20241205.13

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AMA Style

Narimawati U, Sarwono J. Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. Am J Appl Math. 2024;12(5):133-140. doi: 10.11648/j.ajam.20241205.13

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@article{10.11648/j.ajam.20241205.13,
  author = {Umi Narimawati and Jonathan Sarwono},
  title = {Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca
},
  journal = {American Journal of Applied Mathematics},
  volume = {12},
  number = {5},
  pages = {133-140},
  doi = {10.11648/j.ajam.20241205.13},
  url = {https://doi.org/10.11648/j.ajam.20241205.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20241205.13},
  abstract = {The aim of the research is to review theories underlying the Structural Equation Modeling (SEM) procedure based on covariance (CBSEM), partial least square (PLSSEM) and component (GESCA SEM). The methods used are meta-analysis and systematic secondary data search. Results of the study are: First, theories underlying the CBSEM, PLSSEM and GESCA SEM procedures produce different characteristics in each SEM model. CBSEM models consist of two sub models, namely 1) Factor Analysis Model consisting of a) Exploratory Factor Analysis (EFA) which is designed for a situation where the relationship between indicators and latent variables is unknown or unclear; b) Confirmatory Factor Analysis (CFA) which is used for research where the researcher already has knowledge about the structure of the underlying latent variable (construct) and c) Full Latent Variable Model (LV). 2) PLSSEM consists of two sub model, namely reflective and formative models. GESCA SEM consists of structural / inner model and measurement / outer model. Second, the primary characteristics of CBSEM, PLSSEM and GESCA SEM are requirements of the amount of data sample; the sample data origin; and the software used to calculate the data due to the different statistical formulation, namely LISREL, SmartPLS and GSCA Pro Windows. Third, the main differences among the CBSEM, PLS SEM and GESCA SEM are in the uses of the unstandardized regression coefficients (b) versus the standardized regression coefficients (β). Thus, the researchers that are going to use those procedures must consider those three important findings.
},
 year = {2024}
}

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TY  - JOUR
T1  - Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca

AU  - Umi Narimawati
AU  - Jonathan Sarwono
Y1  - 2024/09/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajam.20241205.13
DO  - 10.11648/j.ajam.20241205.13
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 133
EP  - 140
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20241205.13
AB  - The aim of the research is to review theories underlying the Structural Equation Modeling (SEM) procedure based on covariance (CBSEM), partial least square (PLSSEM) and component (GESCA SEM). The methods used are meta-analysis and systematic secondary data search. Results of the study are: First, theories underlying the CBSEM, PLSSEM and GESCA SEM procedures produce different characteristics in each SEM model. CBSEM models consist of two sub models, namely 1) Factor Analysis Model consisting of a) Exploratory Factor Analysis (EFA) which is designed for a situation where the relationship between indicators and latent variables is unknown or unclear; b) Confirmatory Factor Analysis (CFA) which is used for research where the researcher already has knowledge about the structure of the underlying latent variable (construct) and c) Full Latent Variable Model (LV). 2) PLSSEM consists of two sub model, namely reflective and formative models. GESCA SEM consists of structural / inner model and measurement / outer model. Second, the primary characteristics of CBSEM, PLSSEM and GESCA SEM are requirements of the amount of data sample; the sample data origin; and the software used to calculate the data due to the different statistical formulation, namely LISREL, SmartPLS and GSCA Pro Windows. Third, the main differences among the CBSEM, PLS SEM and GESCA SEM are in the uses of the unstandardized regression coefficients (b) versus the standardized regression coefficients (β). Thus, the researchers that are going to use those procedures must consider those three important findings.

VL  - 12
IS  - 5
ER  -

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Author Information

Umi Narimawati

Economic Department, Computer University of Indonesia, Bandung, Indonesia

Contact Email

http://orcid.org/0000-0001-8160-6263
Jonathan Sarwono

Science and Technology Department, International Women University, Bandung, Indonesia

Contact Email

http://orcid.org/0009-0003-1774-2182

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APA Style

Narimawati, U., Sarwono, J. (2024). Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. American Journal of Applied Mathematics, 12(5), 133-140. https://doi.org/10.11648/j.ajam.20241205.13

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ACS Style

Narimawati, U.; Sarwono, J. Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. Am. J. Appl. Math. 2024, 12(5), 133-140. doi: 10.11648/j.ajam.20241205.13

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AMA Style

Narimawati U, Sarwono J. Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca. Am J Appl Math. 2024;12(5):133-140. doi: 10.11648/j.ajam.20241205.13

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@article{10.11648/j.ajam.20241205.13,
  author = {Umi Narimawati and Jonathan Sarwono},
  title = {Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca
},
  journal = {American Journal of Applied Mathematics},
  volume = {12},
  number = {5},
  pages = {133-140},
  doi = {10.11648/j.ajam.20241205.13},
  url = {https://doi.org/10.11648/j.ajam.20241205.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20241205.13},
  abstract = {The aim of the research is to review theories underlying the Structural Equation Modeling (SEM) procedure based on covariance (CBSEM), partial least square (PLSSEM) and component (GESCA SEM). The methods used are meta-analysis and systematic secondary data search. Results of the study are: First, theories underlying the CBSEM, PLSSEM and GESCA SEM procedures produce different characteristics in each SEM model. CBSEM models consist of two sub models, namely 1) Factor Analysis Model consisting of a) Exploratory Factor Analysis (EFA) which is designed for a situation where the relationship between indicators and latent variables is unknown or unclear; b) Confirmatory Factor Analysis (CFA) which is used for research where the researcher already has knowledge about the structure of the underlying latent variable (construct) and c) Full Latent Variable Model (LV). 2) PLSSEM consists of two sub model, namely reflective and formative models. GESCA SEM consists of structural / inner model and measurement / outer model. Second, the primary characteristics of CBSEM, PLSSEM and GESCA SEM are requirements of the amount of data sample; the sample data origin; and the software used to calculate the data due to the different statistical formulation, namely LISREL, SmartPLS and GSCA Pro Windows. Third, the main differences among the CBSEM, PLS SEM and GESCA SEM are in the uses of the unstandardized regression coefficients (b) versus the standardized regression coefficients (β). Thus, the researchers that are going to use those procedures must consider those three important findings.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Theoretical Approaches Review on Covariance Based Sem Using Lisrel, Partial Least Based Sem Using Smart PLS and Component Based Sem Using Gesca

AU  - Umi Narimawati
AU  - Jonathan Sarwono
Y1  - 2024/09/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajam.20241205.13
DO  - 10.11648/j.ajam.20241205.13
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 133
EP  - 140
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20241205.13
AB  - The aim of the research is to review theories underlying the Structural Equation Modeling (SEM) procedure based on covariance (CBSEM), partial least square (PLSSEM) and component (GESCA SEM). The methods used are meta-analysis and systematic secondary data search. Results of the study are: First, theories underlying the CBSEM, PLSSEM and GESCA SEM procedures produce different characteristics in each SEM model. CBSEM models consist of two sub models, namely 1) Factor Analysis Model consisting of a) Exploratory Factor Analysis (EFA) which is designed for a situation where the relationship between indicators and latent variables is unknown or unclear; b) Confirmatory Factor Analysis (CFA) which is used for research where the researcher already has knowledge about the structure of the underlying latent variable (construct) and c) Full Latent Variable Model (LV). 2) PLSSEM consists of two sub model, namely reflective and formative models. GESCA SEM consists of structural / inner model and measurement / outer model. Second, the primary characteristics of CBSEM, PLSSEM and GESCA SEM are requirements of the amount of data sample; the sample data origin; and the software used to calculate the data due to the different statistical formulation, namely LISREL, SmartPLS and GSCA Pro Windows. Third, the main differences among the CBSEM, PLS SEM and GESCA SEM are in the uses of the unstandardized regression coefficients (b) versus the standardized regression coefficients (β). Thus, the researchers that are going to use those procedures must consider those three important findings.

VL  - 12
IS  - 5
ER  -

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