The Pre-Postmortem on House District 49: Say it ain’t so, Joe!

SaundersEven with the problems of Election Night 2014, one of the biggest surprises was the defeat of Joe Saunders in House District 49. After winning the election in a landslide in 2012, he was unable to retain his seat this time around. What exactly went wrong?

As some of you may remember, I said that Joe Saunders would lose in 2012 to Marco Pena. The reason that I made this prediction was because of something I call the “Quinones Effect”. For those of you who may not know, John Quinones was a Republican who won in a heavily Democratic seat in south-central Orange County. I contend that the reason Quinones won was because of heritage links. Quinones is Puerto Rican, and ran in a district where the overwhelming majority of the Hispanic voters were Puerto Rican. His opponents tended not to be Puerto Rican, but of another nationality. As a result, Quinones would win the Puerto Rican vote, thus winning the seat. In the case of Saunders in 2012, I though Democratic Hispanics would vote for Pena, which is the reason I selected Pena as my prediction. This did not happen.

However, it seems as if The Quinones Effect was a part of Saunders’ defeat. While Orange County still does not have complete turnout data (at which time a further statistical examination of the race will be done), it seems as if there might be a negative relationship between the number of Hispanic voters in a precinct and vote percentage for Joe Saunders. Below is a simple scatter plot chart of each precinct. On the y-axis is the percentage of change in Sanders’ vote totals (which is the dependent variable, basically what we are trying to understand). The x-axis shows the amount of Hispanics that are registered in each precinct.


As we can see with this simple scatter plot, it does seem to show that an increase in the amount of Hispanics in a precinct does lead to a greater vote percentage loss for Saunders between 2012 and 2014. While this is only used for visualization, if the function value closely resembles the function on this chart, we might be able to see The Quinones Effect at work.

With white voters, we can assume the opposite relationship occurs since they are the other major ethnic group. This does not mean that Saunders performed better in the white neighborhoods, but that he didn’t suffer as large of a defeat. However, some precincts have significant black populations. So, just for a preliminary look before going into the research later, I decided to run the numbers in regards to black registered voters. Here is what the simple scatter plot looks like:


In this case we are looking at two things. First, what is the slope of the function (which is the line). As we see, the line is horizontal. Therefore, if this function is correct, black voters really had no impact on the Saunders vote total. Secondly, we are not looking at the if the relationship is positive or negative (which doesn’t matter because it is basically horizontal), but to see how much residual exists. Residual is what is left over after running the linear regression model. So, what we are looking for here is how well the function (or the line that you see) can explain the “observed values” (which are the dots). The closer the dots are to the line, the more likely this function can explain the relationship between the observed values (which is the relationship between vote loss/gain for Saunders and the amount of black voters in a precinct). If all of the dots appeared on the line, we would have a perfect linear correlation, and the model would accurately show us what is going on. In the case of, however, we see our observed values all over the chart, and a function that shows no slope at all. Therefore, at first glance, black voters had very little, if any, impact on the Saunders race.

Of course, this is only a basic look at something that might be going on. Also, this data only looks at registered voters in each precinct. Hopefully soon, the Orange County Supervisor of Elections will have turnout data by race and party so that we can truly understand the dynamic going on here. At that time, I will be running an analysis on the data and presenting that here. And because of the lack of attitudinal data for Florida State House races, this aggregate data is the best we can use to try to explain a possible relationship.

Therefore, stay tuned.


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