Figure 1.
Map of Study Region in Puerto Rico.
Municipalities where surveys were conducted are highlighted in gray. We did not list specific communities that we visited to keep the communities we surveyed anonymous.
Table 1.
Description and hypothesized relationship for each of the variables considered in our statistical models.
Table 2.
Comparison of each variable considered in our statistical models by institution type (PRASA vs non-PRASA).
Figure 2.
Parameter Estimate Plot of All Variables Considered in the Two Models that Predict Household Water Management Strategies.
Standard errors are plotted as black lines. The variable is significant if standard error bars do not cross the zero axis. For the number of water sources (A), institution type is significant (p<0.005). For whether households treat water (B), institution type (p<0.001), perceptions of water quality (p<0.05), and the interaction between institution type and if households have a problem with institutional management (p<0.05) are significant.
Table 3.
Results for each statistical model predicting which factors are associated with household water management strategies.
Figure 3.
Importance of Each Covariate for Model Fit in the Two Models that Predict Household Water Management Strategies.
Change in AICc for each of the covariates considered in the full logit model for the number of drinking water sources (A) and whether households treat or do not treat water (B). Larger changes in AICc values suggest that the variable contributed more to overall model fit. In both analyses (A and B), the institutional variable Water System (i.e. PRASA, non-PRASA) is the variable that contributes most to overall model fit. In the analysis of whether households treat water (B), water quality perceptions were also an important variable.