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Trong bối cảnh biến đổi khí hậu và phát triển nông thôn bền vững, đánh giá rủi ro khí hậu cấp xã đóng vai trò quan trọng trong hỗ trợ ra quyết định và xây dựng giải pháp thích ứng. Tuy nhiên, các bộ chỉ số hiện nay vẫn thiếu kiểm định đầy đủ về độ tin cậy và giá trị đo lường. Nghiên cứu này kiểm định độ tin cậy, giá trị và xác định trọng số của bộ chỉ số rủi ro khí hậu cấp xã bằng cách kết hợp Phân tích nhân tố khám phá (EFA) và mô hình PLS-SEM, dựa trên dữ liệu khảo sát từ 145 chuyên gia và đại diện cộng đồng với 45 chỉ số. Kết quả nghiên cứu cho thấy các thang đo đáp ứng các yêu cầu về độ tin cậy, giá trị hội tụ và giá trị phân biệt. Trọng số các chỉ số được xác định từ hệ số tải ngoài trong mô hình PLS-SEM. Bộ chỉ số có cơ sở khoa học và tính ứng dụng cao, góp phần nâng cao độ tin cậy trong đánh giá rủi ro khí hậu cấp xã và hỗ trợ xây dựng giải pháp thích ứng dựa trên bằng chứng.
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Cách trích dẫn
Hoàng Thanh Hùng, Lê Thị Hoa Sen, Trần Anh Tuấn, & Hoàng Dũng Hà. (2026). Ứng dụng EFA và PLS-SEM trong kiểm định và xác định trọng số bộ chỉ số rủi ro khí hậu cấp xã. Tạp Chí điện tử Khoa học Và công nghệ nông nghiệp, 10(3), 5838–5849. https://doi.org/10.46826/huaf-jasat.v10n3y2026.1405
Chuyên mục
KHUYẾN NÔNG - PHÁT TRIỂN NÔNG THÔN
Bản quyền @ 2017-2026 bởi Tạp chí điện tử Khoa học và công nghệ nông nghiệp
Tài liệu tham khảo
Hoàng Trọng và Chu Nguyễn Mộng Ngọc (2008). Phân tích dữ liệu nghiên cứu với SPSS: Tập 1, Nhà xuất bản Hồng Đức.
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
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