Structural Equations Modeling in the Management Sciences
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.5414Keywords:
Structural Equations Models (SEM), management, algorithms, softwareAbstract
This article analyzes the role of structural equation modeling in the field of management science. The approach by which the use of the technique is incorporated into the field of management, additionally, a comparison is generated between the different algorithms and statistical packages available for the application of the technique. Finding in closed source, LISREL, EQS, AMOS, SmartPLS, Mplus, CALIS (in SAS), SEPATH (in Statistica), RAMONA in (Systat), SEM (Stata), Matlab, Semopy (in Python) and in open source, Scilab, Julia, R: lavaan, sem, lava, OpenMx, Strum. Identifying current trends and future approaches that embody the multivariate analysis technique within the management sciences. Finding suggested software for the area of management sciences, as well as mixing trends in the technique, such as the use of neural networks that is beginning to be incorporated as an alternative or complement to the SEM technique.
Downloads
References
Aria, M. (2020). PLS-SEM Toolbox, MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/54147-pls-sem-toolbox.
Alshurideh, M., Al Kurdi, B., Salloum, S., Arpaci, I., & Al-Emran, M. (2020). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 28(4), 1-15.
Bentler, M. (2006). EQS Structural Equations Program Manual. Encino, CA: Multivariante Software, Inc.
Boker, S., Neale, M., Maes, H., Wilde, M., Timothy, M., Estabrook, B., Paras, B. Ross, J. Michael, G., Hunter, D., Karch, H., Pritikin, J., Zahery, M., Kirkpatrick, R., (2021). OpenMx User Guide. https://vipbg.vcu.edu/vipbg/OpenMx2/docs//OpenMx/latest/OpenMxUserGuide.pdf.
Campbell, S., Chancelier, J., & Nikoukhah, R. (2006). Modeling and Simulation in SCILAB. Springer: New York.
Driver, C., Oud, J., & Voelkle, M. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77(5), 24-56.
Gómez, O., López, R., & Bacalla, S. (2010). Criterios de selección de metodologías de desarrollo de software. Industrial Data, 13(2), 70-74.
Hair, J., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2), 44-55.
Haughton, D., Kamis, A., & Scholten, P. (2006). A review of three directed acyclic graphs software packages: MIM, Tetrad, and WinMine. The American Statistician, 60(3), 272-286.
Hoyle, R. (2012). Handbook of structural equation modeling. New York: Guilford press.
Holst, K., & Budtz-Jørgensen, E. (2013). Linear latent variable models: the lava-package. Computational Statistics, 28(4), 1385-1452.
Holst, K., & Budtz-Jørgensen, E. (2019). A two-stage estimation procedure for non-linear structural equation models. Biostatistics, 21(4), 676-691.
Igolkina, A, & Meshcheryakov, G. (2020). Semopy: A Python Package for Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 952-963, https://doi.org/10.1080/10705511.2019.1704289
Jacobucci, R., Grimm, K., & McArdle, J. (2016). Regularized structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(4), 555-566.
Jöreskog, K., & Sörbom, D. (2018). LISREL 10 for Windows [Computer software]. Skokie, IL: Scientific Software International, Inc.
Lee, S. (2015). Implementing a simulation study using multiple software packages for structural equation modeling. SAGE Open, 5(3), 1-15. https://doi.org/10.1177/2158244015591823.
Liébana-Cabanillas, F., Marinkovic, V., de Luna, I., & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129, 117-130.
Mair, P., Wu, E., & Bentler, P. (2010). EQS goes R: Simulations for SEM using the package REQS. Structural Equation Modeling: A Multidisciplinary Journal, 17(2), 333-349.
Manley, S., Hair, J., Williams, R., & McDowell, W. (2020). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, 16(2), 1-21.
Marcoulides, G., & Falk, C. (2018). Model specification searches in structural equation modeling with R. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 484-491.
Merkle, E., Fitzsimmons, E., Uanhoro, J., & Goodrich, B. (2020). Efficient Bayesian Structural Equation Modeling in Stan. https://arxiv.org/abs/2008.07733.
Muthén, B., & Muthén, L. (2017). Mplus, Handbook of Item Response Theory Chapman and Hall/CRC. New York: Routledge.
Narayanan, A. (2012): A Review of Eight Software Packages for Structural Equation Modeling. The American Statistician, 66(2), 129-138.
Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. In Modern statistical methods for HCI, Springer, Cham, 275-287.
Ríos, M., & Idrobo, S. (2017). Análisis comparativo de software matemático para la formación de competencias de aprendizaje en cálculo diferencial. Plumilla educativa, 19(1), 98-113.
Rosseel, Y. (2012). Lavaan: an R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36.
Segura, A., Vidal, C., & Prieto, M. (2008). Evaluación de la Calidad del Software para el Aprendizaje. In X Simposio Internacional de Informática Educativa SIIE, Salamanca, 59-64.
Song, Y., Stein, C., & Morris, N. (2015). Strum: an R package for structural modeling of latent variables for general pedigrees. BMC Genomic Data, 16(35), 1-13. https://doi.org/10.1186/s12863-015-0190-3
Sotnikova-Meleshkina, Z., & Martynenko, O. (2020). A multivariate method of evaluating the effectiveness of optimizing the daily routine of schoolchildren. Reports of Vinnytsia National Medical University, 24(4), 659-664.
Stapleton, L., & Leite, W. (2005). Teacher's corner: A review of syllabi for a sample of structural equation modeling courses. Structural Equation Modeling: A Multidisciplinary Journal, 12(4), 642-664.
Steiger, J. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual differences, 42(5), 893-898.
Tarka, P. (2018). An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52(1), 313-354.
Von Oertzen, T., Brandmaier, A., & Tsang, S. (2015). Structural equation modeling with Ωnyx. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 148-161.
Williams, N. (2020). Toolbox for Structural Equation Modelling (SEM), MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/60013-toolbox-for-structural-equation-modelling-sem.
Yuan, K., & Bentler, P. (1998). Normal theory based test statistics in structural equation modelling, British Journal of Mathematical and Statistical Psychology, 51(2), 289-309.
Yung, Y. (2010). Introduction to structural equation modeling using the CALIS Procedure in SAS/STAT® software, Computer technology workshop presented at the Joint Statistical Meeting on August 4, 2010, Vancouver, Canada. https://support.sas.com/rnd/app/stat/papers/JSM2010_Yung.pdf
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
