Confirmatory Factor Analysis on Climate Change Impact on Human Migration Patterns and Social Vulnerability (International Journal of Engineering and Computer Science, 2024)
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.