Valley International Journal Digital Library

VIJ Digital library
Articles

Confirmatory Factor Analysis on Climate Change Impact on Human Migration Patterns and Social Vulnerability

PDF
  • Atemoagbo, Oyarekhua Precious
Atemoagbo, Oyarekhua Precious
Department of Agricultural and Bioresources Engineering, Federal University of Technology, Minna, Nigeria
Published in : International Journal Of Engineering And Computer Science (IJECS)

Submission to VIJ 2024-09-15

Keywords

  • climate change, human migration, social vulnerability, confirmatory factor analysis, engineering knowledge, problem-solving skills.

Copyright (c) 2024 Luong Hoang Giang

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study conducted a confirmatory factor analysis (CFA) to investigate the impact of climate change on human migration patterns and social vulnerability. The CFA model consisted of two factors: Factor 1 (engineering knowledge) and Factor 2 (problem-solving skills). The model fit indices showed a good fit: χ²/df = 2.35, RMSEA = 0.06, CFI = 0.93, and TLI = 0.92. The factor loadings ranged from 0.44 (SV3) to 0.87 (MP2), indicating moderate to strong relationships between the indicators and their respective factors. The average variance extracted (AVE) values were 0.36 (Factor 1) and 0.41 (Factor 2), indicating adequate convergent validity. The heterotrait-monotrait (HTMT) ratios ranged from 0.63 (MP1) to 1.00 (MP2), indicating good discriminant validity. The residual covariances between indicators ranged from -0.21 (CC2 ↔ SV2) to 0.14 (MP2 ↔ CC1), indicating some remaining relationships between indicators. The misfit plot showed small residuals for most indicators, indicating a good fit between observed and predicted values. Overall, the results suggest that climate change impacts human migration patterns and social vulnerability through two distinct factors: engineering knowledge and problem-solving skills. The findings have implications for policymakers and researchers seeking to understand and address climate change's effects on human migration and social vulnerability.

PDF

References

  1. Abatzoglou, J., Williams, P., & Barbero, R. (2019). Global emergence of anthropogenic climate change in fire weather indices. Geophysical Research Letters, 46(1), 326–336. https://doi.org/10.1029/2018gl080959
  2. Acosta-Michlik, L., & Espaldon, V. (2008). Assessing vulnerability of selected farming communities in the Philippines based on a behavioural model of agent’s adaptation to global environmental change. Global Environmental Change, 18(4), 554–563. https://doi.org/10.1016/j.gloenvcha.2008.08.006
  3. Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. (2020). Chi‐square for model fit in confirmatory factor analysis. Journal of Advanced Nursing, 76(9), 2209–2211. https://doi.org/10.1111/jan.14399
  4. Ardi, R., Hidayatno, A., & Zagloel, T. Y. M. (2012). Investigating relationships among quality dimensions in higher education. Quality Assurance in Education, 20(4), 408–428. https://doi.org/10.1108/09684881211264028
  5. Atemoagbo, O. P. (2024). Investigating The Impact of Sanitation Infrastructure on Groundwater Quality and Human Health in Peri-Urban Areas. International Journal of Medical Science and Clinical Invention, 11(01), 7260–7273. Retrieved from https://valleyinternational.net/index.php/ijmsci/article/view/4695
  6. Atemoagbo, O. P. (2024). Risk Assessment and Remediation Options for Oil-Contaminated Soil and Groundwater: A Comparative Analysis of Chemical, Physical, And Biological Treatment Methods. Research and Analysis Journal, 7(01), 01–11. Retrieved from https://rajournals.com/index.php/raj/article/view/383
  7. Atemoagbo, O. P. (2024); Martins, Y. O.; Animashaun, I. M.; Chukwu, S. E. (2024). Metropolitan Flood Risk Characterization Using Remote Sensing, GIS, and Fuzzy Logic (RS-GIS-Fl) Approach: Suleja, Nigeria. International Journal of Engineering and Computer Science, 13(03), 26101–26111. Retrieved from https://ijecs.in/index.php/ijecs/article/view/4798
  8. Atemoagbo, O. P.; Abdullahi, A.; Siyan P. (2024). Cluster Analysis of MSMES In Suleja, Nigeria: Insights From Fuzzy C-Means Clustering And T-SNE Visualizations. Management and Economic Journal, 1–9. Retrieved from https://everant.in/index.php/mej/article/view/577
  9. Atemoagbo, O. P.; Abdullahi, A.; Siyan, P. (2024). Modeling Economic Relationships: A Statistical Investigation of Trends and Relationships”, Soc. sci. humanities j., vol. 8, no. 05, pp. 3778–3796, Oct. 2024, doi: 10.18535/sshj.v8i05.1039.
  10. Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y. (2010). Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress. American Educational Research Journal, 47(1), 133–180. https://doi.org/10.3102/0002831209345157
  11. Beck, U., & Grande, E. (2010). Varieties of second modernity: the cosmopolitan turn in social and political theory and research. British Journal of Sociology, 61(3), 409–443. https://doi.org/10.1111/j.1468-4446.2010.01320.x
  12. Birkmann, J. (2007). Measuring vulnerability to natural hazards: towards disaster resilient societies. Choice Reviews Online, 45(01), 45–0295. https://doi.org/10.5860/choice.45-0295
  13. Black, R., Adger, W. N., Arnell, N. W., Dercon, S., Geddes, A., & Thomas, D. S. G. (2011). The effect of environmental change on human migration. Global Environmental Change, 21(1), 3-11.
  14. Brown, T. A. (2007). Confirmatory factor analysis for applied research. Choice Reviews Online, 44(05), 44–2769. https://doi.org/10.5860/choice.44-2769
  15. Buhaug, H., & Urdal, H. (2013). An urbanization bomb? Population growth and social disorder in cities. Global Environmental Change, 23(1), 1–10. https://doi.org/10.1016/j.gloenvcha.2012.10.016
  16. Chen, N. F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720
  17. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Structural Equation Modeling a Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/s15328007sem0902_5
  18. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/10.1016/s0022-4359(01)00056-2
  19. Clubb, F. J., Mudd, S. M., Attal, M., Milodowski, D. T., & Grieve, S. W. (2016). The relationship between drainage density, erosion rate, and hilltop curvature: Implications for sediment transport processes. Journal of Geophysical Research Earth Surface, 121(10), 1724–1745. https://doi.org/10.1002/2015jf003747
  20. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
  21. De Lellis, P., Marín, M. R., & Porfiri, M. (2021). Modeling Human Migration Under Environmental Change: A Case Study of the Effect of Sea Level Rise in Bangladesh. Earth S Future, 9(4). https://doi.org/10.1029/2020ef001931
  22. Dell, M., Jones, B. F., & Olken, B. A. (2014). What Do We Learn from the Weather? The New Climate-Economy Literature. Journal of Economic Literature, 52(3), 740–798. https://doi.org/10.1257/jel.52.3.740
  23. Dinev, T., & Hart, P. (2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
  24. Dogra, P., Kaushik, A. K., Kalia, P., & Kaushal, A. (2023). Influence of augmented reality on shopping behavior. Management Decision, 61(7), 2073–2098. https://doi.org/10.1108/md-02-2022-0136
  25. Feng, S., Krueger, A. B., & Oppenheimer, M. (2010). Linkages among climate change, crop yields and Mexico–US cross-border migration. Proceedings of the National Academy of Sciences, 107(32), 14257–14262. https://doi.org/10.1073/pnas.1002632107
  26. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  27. Fritz, K., Schoenmueller, V., & Bruhn, M. (2017). Authenticity in branding – exploring antecedents and consequences of brand authenticity. European Journal of Marketing, 51(2), 324–348. https://doi.org/10.1108/ejm-10-2014-0633
  28. Gao, J., Jiao, K., & Wu, S. (2018). Quantitative assessment of ecosystem vulnerability to climate change: methodology and application in China. Environmental Research Letters, 13(9), 094016. https://doi.org/10.1088/1748-9326/aadd2e
  29. Guo, H., Wang, R., Garfin, G. M., Zhang, A., Lin, D., Liang, Q., & Wang, J. (2020). Rice drought risk assessment under climate change: Based on physical vulnerability a quantitative assessment method. The Science of the Total Environment, 751, 141481. https://doi.org/10.1016/j.scitotenv.2020.141481
  30. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis. Cengage Learning.
  31. Hansen, J., Sato, M., & Ruedy, R. (2016). Ice melt, sea level rise and superstorms: Evidence from paleoclimate data, climate modeling, and modern observations that 2°C global warming above the preindustrial level would be dangerous. Atmospheric Chemistry and Physics, 16(10), 3781-3805.
  32. IPCC (2014). Climate Change 2014: Impacts, Vulnerability, and Adaptation. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  33. Jordan, N., Boody, G., Broussard, W., Glover, J. D., Keeney, D., McCown, B. H., McIsaac, G., Muller, M., Murray, H., Neal, J., Pansing, C., Turner, R. E., Warner, K., & Wyse, D. (2007). Sustainable Development of the Agricultural Bio-Economy. Science, 316(5831), 1570–1571. https://doi.org/10.1126/science.1141700
  34. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
  35. Kapadia, A. S., Chan, W., & Moyé, L. (2017). Mathematical Statistics with Applications. In CRC Press eBooks. https://doi.org/10.1201/9781315275864
  36. Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford Press.
  37. Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based structural equation modeling. Journal of Business Research, 65(8), 1091-1098.
  38. Lankao, P. R., & Qin, H. (2011). Conceptualizing urban vulnerability to global climate and environmental change. Current Opinion in Environmental Sustainability, 3(3), 142–149. https://doi.org/10.1016/j.cosust.2010.12.016
  39. Li, C. (2015). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. https://doi.org/10.3758/s13428-015-0619-7
  40. Liñán, F., & Chen, Y. (2009). Development and Cross–Cultural Application of a Specific Instrument to Measure Entrepreneurial Intentions. Entrepreneurship Theory and Practice, 33(3), 593–617. https://doi.org/10.1111/j.1540-6520.2009.00318.x
  41. Loehlin, J. C. (2003). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. http://ci.nii.ac.jp/ncid/BA68209495
  42. Mao, Z., & Lyu, J. (2017). Why travelers use Airbnb again? International Journal of Contemporary Hospitality Management, 29(9), 2464–2482. https://doi.org/10.1108/ijchm-08-2016-0439
  43. Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), 167–256. https://doi.org/10.1137/s003614450342480
  44. Nwoke, L. I. (2016). The Psychological Impact of Live Broadcasting On Mental Health: A Comparative Study of Radio And Television Presenters. (2016). International Journal of Scientific Research and Management (IJSRM), 4(9), 4636-4646. https://doi.org/10.18535/ijsrm/v4i9.21
  45. Nwoke, L. I. (2017). Social Media Use and Emotional Regulation in Adolescents with Autism Spectrum Disorder: A Longitudinal Examination of Moderating Factors. International Journal of Medical Science and Clinical Invention, 4(3), 2816–2827. Retrieved from https://valleyinternational.net/index.php/ijmsci/article/view/2555
  46. Nwoke, L. I., Precious, A. O., Aisha, A., & Peter, S. (2022). The Impact of Cashless Policy on the Performance of Msmes in Nigeria Using Artificial Neural Network. International Journal of Social Sciences and Humanities Invention, 9(08), 7182–7193. https://doi.org/10.18535/ijsshi/v9i08.09
  47. Parmesan, C. (2006). Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology Evolution and Systematics, 37(1), 637–669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100
  48. Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2012). Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling. Educational and Psychological Measurement, 73(1), 5–26. https://doi.org/10.1177/0013164412449831
  49. Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2). https://doi.org/10.18637/jss.v048.i02
  50. Santamouris, M. (2020). Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy and Buildings, 207, 109482. https://doi.org/10.1016/j.enbuild.2019.109482
  51. Scoones, I. (2009). Livelihoods perspectives and rural development. The Journal of Peasant Studies, 36(1), 171–196. https://doi.org/10.1080/03066150902820503
  52. Semyonov, M., Raijman, R., & Gorodzeisky, A. (2006). The Rise of Anti-foreigner Sentiment in European Societies, 1988-2000. American Sociological Review, 71(3), 426–449. https://doi.org/10.1177/000312240607100304
  53. Sepasgozar, S. M. (2022). Immersive on-the-job training module development and modeling users’ behavior using parametric multi-group analysis: A modified educational technology acceptance model. Technology in Society, 68, 101921. https://doi.org/10.1016/j.techsoc.2022.101921
  54. Singh, N. J., Börger, L., Dettki, H., Bunnefeld, N., & Ericsson, G. (2012). From migration to nomadism: movement variability in a northern ungulate across its latitudinal range. Ecological Applications, 22(7), 2007–2020. https://doi.org/10.1890/12-0245.1
  55. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. http://eprints.ioe.ac.uk/3525/
  56. Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, D. O. T., Vanderborght, J., Young, M. H., Amelung, W., Aitkenhead, M., Allison, S. D., Assouline, S., Baveye, P., Berli, M., Bruggemann, N., Finke, P., Flury, M., Gaiser, T., Govers, G., . . . Young, I. M. (2016). Modeling Soil Processes: Review, Key Challenges, and New Perspectives. Vadose Zone Journal, 15(5), 1–57. https://doi.org/10.2136/vzj2015.09.0131
  57. Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Liermann, C. R., & Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. https://doi.org/10.1038/nature09440
  58. Vulnerability to Global Environmental Change. (2014). In Routledge eBooks (pp. 261–301). https://doi.org/10.4324/9781849772563-26
  59. Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042
  60. Žurovec, O., Čadro, S., & Sitaula, B. K. (2017). Quantitative Assessment of Vulnerability to Climate Change in Rural Municipalities of Bosnia and Herzegovina. Sustainability, 9(7), 1208. https://doi.org/10.3390/su9071208