Applications of Structural Equation Modelling in Social Sciences Research

Wang Jing; Ali Salman

doi:10.62051/ijsspa.v6n3.17

Authors

Wang Jing
Ali Salman

DOI:

https://doi.org/10.62051/ijsspa.v6n3.17

Keywords:

Structural Equation Modeling, Social Sciences, Multivariate Analysis, Research Methodology, Model Validation

Abstract

Structural Equation Modeling (SEM) has emerged as a powerful analytical tool in social sciences research, offering a robust approach to examining complex relationships among observed and latent variables. Unlike traditional regression models, SEM enables researchers to simultaneously analyze multiple dependent and independent variables, account for measurement errors, and evaluate theoretical models comprehensively. This paper provides an overview of SEM applications in social sciences, emphasizing its advantages, key methodological steps, and limitations.

Downloads

Download data is not yet available.

References

[1] Bollen, Kenneth A. “Structural equations with latent variables”, Applied John Wiley & Sons, 1989. DOI: https://doi.org/10.1002/9781118619179

[2] Byrne, Barbara M. “Structural equation modeling with Mplus: Basic concepts, applications, and programming”, Applied Routledge, 2013. DOI: https://doi.org/10.4324/9780203807644

[3] Jeff Risher, Marko Sarstedt, Christian M. Ringle. “When to use and how to report the results of PLS-SEM”, Applied European Business Review, Vol.31, No.1, pp. 2-24, 2019. https://doi.org/10.1108/EBR-11-2018-0203 DOI: https://doi.org/10.1108/EBR-11-2018-0203

[4] Rex B. Kline, “Principles and practice of structural equation modeling”, Applied Guilford Publications, 2023.

[5] Li‐tze Hu, Peter M. Bentler. “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives”, Applied Structural Equation Modeling: A Multidisciplinary Journal, Vol.6, Issue.1, pp 1-55, 1999. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118

[6] Principles and practice of structural equation modeling. Guilford publications, 2023. Nicole Franziska Richter, Rudolf R. Sinkovics, Christian M. Ringle, Christopher Schlägel. “A critical look at the use of SEM in international business research” Applied International Marketing Review, Vol.33, Issue.3, pp 376-404. https://doi.org/10.1108/IMR-04-2014-0148 DOI: https://doi.org/10.1108/IMR-04-2014-0148

[7] Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson. “Multivariate data analysis”, Applied Cengage, 2019.

[8] Marley W Watkins. “Exploratory Factor Analysis: A Guide to Best Practice”, Applied Black psychology, Vol.44, Issue.3, pp 219-246, 2018. https://doi.org/10.1177/0095798418771807 DOI: https://doi.org/10.1177/0095798418771807

[9] Darmaraj Sakaria, Siti Mistima Maat, Mohd Effendi Ewan Mohd Matore. “Examining the Optimal Choice of SEM Statistical Software Packages for Sustainable Mathematics Education: A Systematic Review”, Applied Sustainability, Vol.15, Issue.4, pp 47-71, 2023. https://doi.org/10.3390/su15043209 DOI: https://doi.org/10.3390/su15043209

[10] Damian Gallagher, Lucy Ting, Adrian Palmer. “A journey into the unknown; taking the fear out of structural equation modeling with AMOS for the first-time user”, Applied The Marketing Review, Vol. 8, No. 3, pp. 255-275, 2008. https://doi.org/10.1362/146934708X337672 DOI: https://doi.org/10.1362/146934708X337672

[11] Karl G. Jöreskog. “Structural analysis of covariance and correlation matrices”, Applied Psychometrika, Vol. 43, Issue. 4, pp. 443-477,1978. https://doi.org/10.1007/BF02293808 DOI: https://doi.org/10.1007/BF02293808

[12] Kenneth A. Bollen, John Scott Long. “Testing structural equation models”, Applied Sage, 1993.

[13] Daire, H., C. Joseph, and R.M. Michael, “Structural Equation Modelling: Guidelines for Determining Model Fit Structural equation modelling: guidelines for determining model fit”, Applied Electron J Bus Res Methods, Vol.6, pp.53–60, 2008.