The central hypothesis of this study is that a holistic, systems-based approach employing multiple analytical tools is useful for identifying the most sensitive links within complex communities to down-scale global development priorities such as the United Nations Sustainable Development Goals. Results of latent factor regression, canonical correlation analysis, and structural equation modeling were compared for multiple, publically-available data sets for two rural regions in Brazil and Guatemala. The results of this study confirm previously reported findings, and collectively support the central hypothesis demonstrating a pathway for linking global priorities with the complex realities of 'on-the-ground' development conditions in specific communities.
- Correlation methods,
- Factor analysis,
- Large scale systems,
- Analytical tool,
- Canonical correlation analysis,
- Complex environments,
- Complex reality,
- Global development,
- Latent factor,
- multidimensional poverty,
- Structural equation modeling,
- Sustainable development,
- complex systems,
- latent factor regression
Available at: http://works.bepress.com/daniel_oerther/76/