In the months — and sometimes years — leading up to a major election, political scientists across the nation engage in tireless forecasting exercises. The question is, how accurate are all these election forecasting models, and how can that accuracy be improved so political organizations gain valuable insights into voter behavior?
In this exploration of election forecasting, we examine how forecasting works. In doing so, we also discuss its accuracy and outline factors that influence the validity of the predictions it produces.
How Does Election Forecasting Work?
When predicting the outcome of elections, political scientists employ a variety of tactics and methodologies. The most common are the sentiment-based approach, the demographic and economic fundamentals-based method, and the polling-based method.
Under the polling-based model, scientists poll voters and then extrapolate that data out on a national scale in the hopes of predicting constituent behavior. Both the other common models also seek to extrapolate data on a nationwide scale. However, the data they use to do so is drastically different.
In the demographic and economic fundamentals-based method, researchers consider how current economic factors influence voting behavior among constituents of different races, ages, and genders. And the sentiment-based method focuses on analyzing how voters feel about candidates and key issues.
Many political scientists use a combination of these models, as well as other data analytics tools, in their attempts to predict election outcomes.
Is Election Forecasting Accurate?
Somewhat, yes. Election forecasting has accurately predicted the outcome of major elections on numerous occasions, including the outcome of several presidential bids.
For instance, PollyVote, a popular forecasting tool, was used to predict the outcome of presidential elections from 2004 to 2016. The platform not only predicted a winner, though; it also estimated what percentage of the popular vote the winning nominee would receive.
In 2004, 2008, and 2012, PollVote correctly forecasted the winner of each election. The platform also had a margin of error of less than 1 percentage point when estimating what percent of the popular vote the winner would receive.
While PollyVote is a great example of the accuracy of election forecasting tools, it — along with dozens of other forecasting models — got things wrong in 2016. The discrepancy between predictions and the results of the 2016 presidential race demonstrated that election forecasting is by no means an infallible science.
Factors that Impact Election Forecast Accuracy
Elections, especially presidential races, are highly complex affairs that involve a virtually incalculable number of variables. As such, it is difficult to accurately forecast their outcomes.
That said, multiple factors can impact forecasting accuracy. By accounting for all of them, political scientists can improve the quality of their forecasting models and increase the accuracy of their predictions. The variables that forecasters must address include the following:
Sample Size and Quality
The combined quality and size of a polling sample or dataset directly impacts the accuracy of an election forecast. Imagine conducting a poll that consists of only registered Democrats or registered Republicans. The outcome of such a poll would suggest that one candidate is going to win by a landslide.
To access valid insights, political scientists must ensure that they are using a diverse set of data that includes multiple demographics. The broader and more diverse the dataset, the more representative it will be of the actual voter base.
Disparities Between Registered Voters vs. Those Who Actually Vote
When performing election outcome forecasting, political scientists often gather an abundance of data from registered voters. However, just because someone is registered to vote does not mean they are actually going to cast a ballot when election day rolls around.
The discrepancy between registered voters and those who actually show up to the polls can skew election forecasts. As a result, they can lead to erroneous predictions — like the world saw in 2016.
Another consideration is undecided voters. People who are on the fence about which candidate they will vote for may tell pollsters one thing and then do another on election day. This swing can shift election outcomes and lead to discrepancies between forecasts and real-world results.
The Type of Model Used
The type of model (or models) that forecasters use has a direct impact on the accuracy of the prediction. In general, the more variables that the model accounts for, the more accurate its results will be. However, all of the major models are prone to errors, which is why election forecasting is far from an exact science.
The good news is that a group of researchers has recently developed a novel statistical model that appears to be more reliable than existing forecasting methodologies. The new approach accounts for “known unknowns,” which refers to the many factors that influence election outcomes but that are difficult to incorporate into a standard model.
It remains to be seen whether this new model will truly be more reliable than existing forecasting tools. However, researchers will have a prime opportunity to put that to the test in the 2024 presidential election cycle.
Tap Into the Right Data with Aristotle
Although the accuracy of election forecasting tools can vary, performing forecasting is a critical part of campaign planning. To facilitate effective, timely, and accurate forecasting, you need access to an abundance of high-quality data.
As a leading political data provider, Aristotle can support your forecasting efforts by connecting you with tens of millions of voter and consumer files. Connect with our team to learn more.