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Abstract
In the context of climate change and the growing need for sustainable rural development, assessing climate risk at the commune level plays a critical role in supporting decision-making and informing adaptation strategies. However, existing indicator sets still lack adequate validation of their reliability and measurement validity. This study aims to assess the reliability and validity, and to determine the weights of a commune-level climate risk index by integrating Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM), based on survey data collected from 145 experts and community representatives across 45 indicators. The findings show that the measurement scales exhibit satisfactory reliability and adequate convergent and discriminant validity. Indicator weights are derived from outer loadings in the PLS-SEM model, reflecting their relative contributions to the overall climate risk construct. The validated index demonstrates strong scientific robustness and practical applicability, thereby enhancing the reliability of climate risk assessment at the commune level and supporting evidence-based adaptation planning.
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How to Cite
Hoang Thanh Hung, Le Thi Hoa Sen, Tran Anh Tuan, & Hoang Dung Ha. (2026). An application of EFA and PLS-SEM in validating and weighting a commune-level climate risk index. E-Journal of Agricultural Science and Technology, 10(3), 5838–5849. https://doi.org/10.46826/huaf-jasat.v10n3y2026.1405
Section
AGRICULTURAL EXTENSION - RURAL DEVELOPMENT
Copyright @ 2017-2026 by E-Journal of Agricultural Science and Technology
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Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. Retrieved from https://openlibrary.org/books/OL1413455M/Psychometric_theory
Simpson, N. P., Mach, K. J., Constable, A., Hess, J., Hogarth, R., Howden, M., et al. (2021). A framework for complex climate change risk assessment. One Earth, 4(4), 489–501.DOI: https://doi.org/10.1016/j.oneear.2021.03.005
Sohail, M. T., & Chen, S. (2022). A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods. Frontiers in Plant Science, 13, 990785.DOI: https://doi.org/10.3389/fpls.2022.990785
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education. Retrieved from https://openlibrary.org/books/OL18267755M/Using_multivariate_statistics
Tate, E. (2012). Social vulnerability indices: A comparative assessment using uncertainty and sensitivity analysis. Natural Hazards, 63, 325–347.DOI: https://doi.org/10.1007/s11069-012-0152-2
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Personality Assessment. DOI: https://doi.org/10.1177/0095798418771807
Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281.DOI: https://doi.org/10.1016/j.gloenvcha.2006.02.006
Bartlett, M. S. (1954). A note on the multiplying factors for various chi square approximations. Journal of the Royal Statistical Society: Series B, 16(2), 296–298. DOI:https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates. Retrieved from https://www.routledge.com/A-First-Course-in-Factor-Analysis/Comrey-Lee/p/book/9781138965454
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2008). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261.DOI: https://doi.org/10.1111/1540-6237.8402002
de Sherbinin, A., Bukvic, A., Rohat, G., Gall, M., McCusker, B., Preston, B., Apotsos, A., Fish, C., Kienberger, S., Muhonda, P., Wilhelmi, O., & Macharia, D. (2019). Climate vulnerability mapping: A systematic review and future prospects. WIREs Climate Change, 10(5), e600. DOI: https://doi.org/10.1002/wcc.600
FAO. (2017). Climate-smart agriculture sourcebook. Food and Agriculture Organization of the United Nations. Retrieved from https://www.fao.org/climate-smart-agriculture-sourcebook
Finch, W. H. (2023). A comparison of methods for determining the number of factors to retain with exploratory factor analysis of dichotomous data. Psych, 5(3), 1004–1018.DOI: https://doi.org/10.3390/psych5030067
Füssel, H. M. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. Global Environmental Change, 17(2), 155–167. DOI: https://doi.org/10.1016/j.gloenvcha.2006.05.002
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94.DOI: https://doi.org/10.1007/s11205-017-1832-9
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall. Retrieved from. https://pdfroom.com/books/multivariate-data-analysis-7th-edition/andLVlXK2e3?utm_source
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115–135.DOI: https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. DOI: https://doi.org/10.1108/S1474-7979(2009)0000020014
Hinkel, J. (2011). Indicators of vulnerability and adaptive capacity. Global Environmental Change, 21(1), 198–208. DOI: https://doi.org/10.1016/j.gloenvcha.2010.08.002
Imran, M., Hossain, M. S., Kabir, M. H., & Rahman, M. T. (2019). Mapping flood vulnerability using PLS-SEM. International Journal of Disaster Risk Reduction, 41, 101288.DOI: https://doi.org/10.1016/j.ijdrr.2019.101288
Intergovernmental Panel on Climate Change (IPCC). (2014). Climate Change 2014: Impacts, adaptation and vulnerability. Cambridge University Press. DOI:https://doi.org/10.1017/CBO9781107415379
Intergovernmental Panel on Climate Change (IPCC). (2022). Climate Change 2022: Impacts, adaptation and vulnerability. Cambridge University Press.DOI: https://doi.org/10.1017/9781009325844
Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111–117. DOI:https://doi.org/10.1177/001316447403400115
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. Retrieved from https://www.guilford.com/books/Principles-and-Practice-of-Structural-Equation-Modeling/Rex-Kline/9781462523344
Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory. IEEE Transactions on Professional Communication, 57(2), 123–146.DOI: https://doi.org/10.1109/TPC.2014.2312452
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffmann, A., & Giovannini, E. (2008). Handbook on constructing composite indicators. OECD.DOI: https://doi.org/10.1787/9789264043466-en
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. Retrieved from https://openlibrary.org/books/OL1413455M/Psychometric_theory
Simpson, N. P., Mach, K. J., Constable, A., Hess, J., Hogarth, R., Howden, M., et al. (2021). A framework for complex climate change risk assessment. One Earth, 4(4), 489–501.DOI: https://doi.org/10.1016/j.oneear.2021.03.005
Sohail, M. T., & Chen, S. (2022). A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods. Frontiers in Plant Science, 13, 990785.DOI: https://doi.org/10.3389/fpls.2022.990785
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education. Retrieved from https://openlibrary.org/books/OL18267755M/Using_multivariate_statistics
Tate, E. (2012). Social vulnerability indices: A comparative assessment using uncertainty and sensitivity analysis. Natural Hazards, 63, 325–347.DOI: https://doi.org/10.1007/s11069-012-0152-2
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Personality Assessment. DOI: https://doi.org/10.1177/0095798418771807