| Issue |
ITM Web Conf.
Volume 67, 2024
The 19th IMT-GT International Conference on Mathematics, Statistics and Their Applications (ICMSA 2024)
|
|
|---|---|---|
| Article Number | 01051 | |
| Number of page(s) | 11 | |
| Section | Mathematics, Statistics and Their Applications | |
| DOI | https://doi.org/10.1051/itmconf/20246701051 | |
| Published online | 21 August 2024 | |
Analysis of socio-economic factors affecting coastal community preparedness using structural equation modeling
1 Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Syiah Kuala, Jl. Tgk. Syech Abdul Rauf, Darussalam, Banda Aceh, 23111, Indonesia
2 Department of Civil Engineering, Faculty of Engineering, Universitas Syiah Kuala, Jl. Tgk. Syech Abdul Rauf, Darussalam, Banda Aceh, 23111, Indonesia
3 Tsunami and Disaster Mitigation Research Center (TDMRC), Jl. Hamzah Fansuri No.8, Darussalam, Banda Aceh, 23111, Indonesia
4 Graduate Program in Disaster Science, Universitas Syiah Kuala, Jl. Hamzah Fansuri No. 4, Darussalam, Banda Aceh, 23111, Indonesia
5 Faculty of Medicine, Universitas Syiah Kuala, Jl. Tgk. Syech Abdul Rauf, Darussalam, Banda Aceh, 23111, Indonesia
* Corresponding author: hizir@usk.ac.id
Preparedness refers to the actions taken before a disaster to ensure an effective response. In disaster-related research, quantitative studies typically focus on observing direct correlations and regressions. However, directly measuring preparedness can be challenging. To comprehensively analyze variables, researchers often turn to Structural Equation Modeling (SEM), a powerful alternative. SEM is particularly useful when examining complex relationships among multiple variables. In a study focused on coastal communities in the cities of Banda Aceh, Mataram, and Ambon, the SEM method was applied using secondary data. The research considered one endogenous latent variable called “preparedness” and two exogenous latent variables related to social and economic factors, which are involving a collective of 932 participants. The results from the SEM method using GOFI criteria indicated that socio-economic factors significantly influenced coastal community readiness, with an R-squared value of 56.5%.
© The Authors, published by EDP Sciences, 2024
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