Likely Voters Models: The Key to Accurate Electoral Analysis

This article is an excerpt from Polling: Statistical Case Studies for Political and Public Affairs Research by Michael D. Lieberman, available on Amazon. Michael is the founder and president of Multivariate Solutions, a New York-based data science and strategy firm, and a guest author for the Elder Research blog.

In the fiercely contested 2024 election, 245 million Americans were registered to vote. However, as reported by U.S. News & World Report, about 90 million of them—roughly 37%—did not cast a ballot. This underscores a critical limitation of polls based on registered voters: they inherently include a substantial margin of error, making it nearly impossible to achieve an electoral forecast with a ±3% margin of accuracy.

In close elections like 2024, this discrepancy can create the impression that the polls were “wrong.” Yet, as ABC News noted, “the average poll conducted during the final three weeks of the campaign missed the election margin by just 2.94 percentage points.”

The average poll had a 3% margin of error, meaning some polls performed better, offering greater predictive accuracy. These more reliable polls are typically based on ‘likely voters’ rather than simply ‘registered voters.’

Polling error differs between samples of likely voters and registered voters due to variations in voter turnout assumptions, modeling techniques, and the composition of the sample. The average polling error for likely voter samples is typically around 2-3%. In contrast, surveys based on registered voters generally exhibit higher error rates, at least one percentage point greater, typically around 4-5%—as this group includes individuals who are less likely to vote.

Here we explore the most commonly used likely voter models, detailing how they identify individuals most likely to participate in an election. It examines methodologies such as self-reported voting intentions, past voting behavior, and demographic indicators. By analyzing these approaches, the chapter highlights their strengths, limitations, and effectiveness in enhancing polling accuracy.

All major polling firms employ likely voter models. These models integrate demographic data, past voting behavior, and survey responses. Approaches vary, from simple self-reported likelihood to sophisticated algorithms. Accurate likely voter models are critical for reliable polling results.

The most famous likely voter model, developed more than a half century ago by Gallup in use today by the Pew Research Center, estimates voter turnout through survey questions on voting likelihood, past behavior, interest, and knowledge. Responses are scored and weighted based on demographics such as age, education, and race. Calibrated to turnout trends, this model effectively identifies likely voters and predicts election outcomes.

Figure 1 – Pew Research Center’s American Trends Panel Weighting Dimensions

No likely voter model remains consistently the most accurate. Much like a hedge fund manager who initially outperforms market indices but eventually regresses to the mean, the effectiveness of a likely voter model fluctuates depending on the election, demographic trends, and data quality. While a model may excel at predicting turnout in certain elections, it can also misjudge key groups or levels of voter enthusiasm. No single model maintains top accuracy across multiple election cycles.

A likely voter model predicts voter turnout by incorporating key elements: voter registration status, past voting behavior, demographics (age, income, education), political interest, partisanship, poll responses, early voting history, state turnout trends, election-specific factors (e.g., competitiveness), and survey weighting techniques to estimate actual turnout probabilities accurately.

Below are the key elements of an effective likely voter model survey. Within this framework, political statisticians can evaluate the most effective model for a given electoral cycle.

Figure 2 – Elements of a Likely Voter Model Questionnaire

Derived metrics play a crucial role in the process as well. For instance, a turnout propensity score provides a numerical prediction of an individual’s likelihood to vote based on model inputs. Vote history matches past voting records with survey data, enabling a more comprehensive analysis. Additionally, demographic weighting adjusts the model to account for population or sample demographics, ensuring greater accuracy and representativeness.

Likely Voter Models utilize predictive analytics to estimate voter turnout by analyzing historical data through statistical algorithms and machine learning. Predictive models often integrate tools like voter turnout scores or microtargeting strategies. With advancements in computing power and open-source tools like the R programming languages, the new power of Excel, or the ubiquitous SPSS, a few lines of code can now streamline data processing, focusing efforts only on those most likely to vote.

Table 1 – Most Common Predictive Models for Likely Voters

International case studies showcase the adaptability and versatility of likelihood voting models in diverse political settings. These examples illustrate how such models are tailored to different electoral systems, utilizing local data inputs like demographics, voter turnout trends, and socioeconomic factors. By doing so, likelihood voting models effectively inform voter mobilization efforts and campaign strategies on a global scale. Table 2 (below) highlights some of the most notable applications from the past decade.

Table 2 – International Case Studies of Predictive Likelihood Voters Models

Identifying likely voters is a critical component of accurate political research and election forecasting. As this chapter has demonstrated, the mathematical models used to predict voter turnout are invaluable tools, but they are not infallible. While self-reported intentions, past behavior, and demographic indicators provide powerful insights, their effectiveness hinges on the context of the election, the quality of data, and the evolving dynamics of the electorate.

No single model works universally; each has strengths and limitations that researchers must weigh carefully. The key to improving accuracy lies in adapting methodologies to new trends, refining assumptions, and acknowledging the inherent uncertainties in human behavior. Ultimately, the pursuit of the “perfect” likely voter model may be elusive, but the iterative process of refinement and innovation ensures that these tools remain indispensable in the ever-changing landscape of political research.