NAVIGATING THE FUTURE OF QUANTITATIVE RESEARCH : THE POWER OF STUCTURAL EQUATION MODELING
บทคัดย่อ
This study delves into the advanced applications of Structural Equation Modeling (SEM) in modern quantitative research. SEM's versatility and power allow researchers to simultaneously examine multiple relationships and account for measurement errors, offering significant advantages over traditional regression models. This research highlights SEM's capacity to provide detailed and nuanced insights into complex constructs, particularly beneficial in social sciences, business administration, and psychology. A rigorous preparatory process is essential for the robustness and reliability of SEM models. This process includes defining the research problem, conducting a comprehensive literature review, developing a theoretical framework, identifying relevant variables, designing the study, and validating measurement instruments. Evaluating the measurement model fit using various indices, such as the Chi-Square Test, RMSEA, CFI, TLI, SRMR, GFI, and AGFI, ensures a comprehensive model accuracy assessment. The findings underscore the significant implications of SEM for advancing quantitative research methodologies. Researchers can enhance their studies' precision and explanatory power by leveraging SEM. This approach paves the way for exploring intricate relationships and contributes to developing sophisticated and reliable research techniques. This study provides an example process, valuable insights, and practical recommendations for researchers aiming to employ advanced statistical methods, ultimately leading to more robust and insightful findings in various research domains.
References
Bollen, K. A. (1989). Structural Equations with Latent Variables. John Wiley & Sons.
Bollen, K. A., Gates, K. M., & Luo , L. (2024). A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA). Psychometrika, 89(1), 687–716.
Brown, T. A. (2015). Confirmatory factor analysis for applied research, 2nd ed. Guilford Publications.
Byrne, B. M. (2016). Structural Equation Modeling With AMOS 3rd Edition: Basic Concepts, Applications, and Programming, Third Edition. New York: Routledge.
Devellis, R. F. (2016). Scale Development: Theory and Applications. Sage Publications.
Fabrigar, L. R., & Wegener, D. T. (2011). Exploratory Factor Analysis. Oxford University Press.
Fu, C., Wang, J., Qu, Z., Skitmore, M., Yi, J., Sun, Z., & Chen, J. (2024). Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review. Sustainable Construction Engineering Processes, 16(9), 1–15. doi:https://doi.org/10.3390/su16093824
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.
Hu, L.‐t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
Kurtaliqi, F., Miltgen, C. L., Viglia, G., & Pantin-Sohier, G. (2024). Using advanced mixed methods approaches: Combining PLS-SEM and qualitative studies. Journal of Business Research, 172(2024), 1–14. doi:https://doi.org/10.1016/j.jbusres.2023.114464
Lesia, M. P., Aigbavboa, C. O., & Thwala, W. D. (2023). Factors influencing residential location choice in South Africa: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Journal of Housing and the Built Environment, 39(1), 133–160.
Little, T. D. (2023). Longitudinal Structural Equation Modeling. New York: The Guilford Press.
Schumacker, R. E., & Lomax, R. G. (2015). A Beginner's Guide to Structural Equation Modeling 4th. Edition. New York: Routledge. doi:https://doi.org/10.4324/9781315749105
Sharma, L., Bulsara, H. P., Bagdi, H., & Trivedi, M. (2024). Exploring sustainable entrepreneurial intentions through the lens of the theory of planned behavior: a PLS-SEM approach. Journal of Advances in Management Research, 21(1), 20–43. doi:https://doi.org/10.1108/JAMR-01-2023-0006
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
Vaithilingam, S., Ong, C. S., Moisescu, O. I., & Nair, M. S. (2024). Robustness checks in PLS-SEM: A review of recent practices and recommendations for future applications in business research. Journal of Business Research, 172(2024), 1–15. doi:https://doi.org/10.1016/j.jbusres.2023.114465
Westland, C. J. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. doi:https://doi.org/10.1016/j.elerap.2010.07.003
Zheng, B. Q., & Bentler, P. M. (2024). Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests. Structural Equation Modeling: A Multidisciplinary Journal, 1–6. doi:https://doi.org/10.1080/10705511.2024.2354802