Correlaton between differnet demographics

Showing overlap and need to condense into factors

Constructing factors

psych::VSS.scree(cor(cordat))

factanal(x = cordat, factors = 2, rotation = "varimax")

## 
## Call:
## factanal(x = cordat, factors = 2, rotation = "varimax")
## 
## Uniquenesses:
##     unemp   poverty    medinc  ownerocc     black h.density        hs 
##     0.497     0.176     0.434     0.005     0.730     0.807     0.491 
## 
## Loadings:
##           Factor1 Factor2
## unemp      0.709         
## poverty    0.867  -0.268 
## medinc    -0.725   0.203 
## ownerocc  -0.454   0.888 
## black      0.515         
## h.density         -0.438 
## hs        -0.710         
## 
##                Factor1 Factor2
## SS loadings      2.756   1.104
## Proportion Var   0.394   0.158
## Cumulative Var   0.394   0.551
## 
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 17285.46 on 8 degrees of freedom.
## The p-value is 0

fitAfterRotation <- factanal(cordat, factors = 2, rotation = "varimax")
print(fitAfterRotation$loadings, cutoff = .40, sort = TRUE)

## 
## Loadings:
##           Factor1 Factor2
## unemp      0.709         
## poverty    0.867         
## medinc    -0.725         
## black      0.515         
## hs        -0.710         
## ownerocc  -0.454   0.888 
## h.density         -0.438 
## 
##                Factor1 Factor2
## SS loadings      2.756   1.104
## Proportion Var   0.394   0.158
## Cumulative Var   0.394   0.551

f <- factanal(cordat, factors=2, rotation="varimax", scores="regression")
dat2 <- cbind(dat2, f$scores)

Relationship Between Embededness of Board Members and Nonprofit Locations

plot(aggdat$dist.to.npo, aggdat$Factor1, col="white", bty="l",
     ylab="Vulnerability Index", xlab="Distance of Board Members to NPO",
      main="Figure 4. Distance Between Board Members and NPO and Vulnerability")
text(aggdat$dist.to.npo, aggdat$Factor1, labels=aggdat$labels, cex=.6)

plot(aggdat$dist.to.npo, aggdat$Factor2, col="white", bty="l",
     ylab="Urbanity Index", xlab="Distance of Board Members to NPO",
     main="Figure 5. Distance Between Board Members and NPO and Urbanity")
text(aggdat$dist.to.npo, aggdat$Factor2, labels=aggdat$labels, cex=.6)

Relationship Between Spending and Nonprofit Locations

plot(aggdat$Onethirdsupportpublic, aggdat$Factor1, col="white", bty="l",
     ylab="Vulnerability Index", xlab="1/3 Support from Public (%)",
     main="Figure 6. One Third Funding from Public and Vulnerability")
text(aggdat$Onethirdsupportpublic, aggdat$Factor1, labels=aggdat$labels, cex=.6)

plot(aggdat$Onethirdsupportpublic, aggdat$Factor2, col="white", bty="l",
     ylab="Vulnerability Index", xlab="1/3 Support from Public (%)",
     main="Figure 7. One Third Funding from Public and Urbanity")
text(aggdat$Onethirdsupportpublic, aggdat$Factor2, labels=aggdat$labels, cex=.6)

plot(aggdat$Onethirdsupportgifts, aggdat$Factor1, col="white", bty="l",
     ylab="Vulnerability Index", xlab="1/3 Support from Gifts (%)",
     main="Figure 8. One Third Funding from Gifts and Vulnerability")
text(aggdat$Onethirdsupportgifts, aggdat$Factor1, labels=aggdat$labels, cex=.6)

plot(aggdat$Onethirdsupportgifts, aggdat$Factor2, col="white", bty="l",
     ylab="Vulnerability Index", xlab="1/3 Support from Gifts (%)",
     main="Figure 9. One Third Funding from Gifts and Urbanity")
text(aggdat$Onethirdsupportgifts, aggdat$Factor2, labels=aggdat$labels, cex=.6)