Beyond Bias: How Advanced Techniques Are Revolutionizing Non-Probability Polling
What 2024 taught us about making non-probability polling as reliable as probability-based methods.
The Pew Research Center’s study, “Comparing Two Types of Online Survey Samples,” highlights persistent challenges with non-probability polling, particularly opt-in online samples, which tend to exhibit greater error compared to probability-based methods.
For estimates among U.S. adults on 28 benchmark variables, opt-in samples 1, 2 and 3 had average absolute errors of 6.4, 6.1 and 5.0, respectively, for an overall average of 5.8 percentage points. This was about twice that of the probability-based online panels, for which average absolute error was 2.6 points overall (2.3, 3.0 and 2.5 on probability panels 1, 2 and 3, respectively).
These findings are no surprise, but they also underscore the growing need for methodological innovation in public opinion research. In 2024, we embraced this challenge, adopting advanced techniques to refine the accuracy and reliability of non-probability polling.
By leveraging tools like super-population modeling, propensity score weighting, and voter file-based calibration, we demonstrated that non-probability polling can overcome its traditional weaknesses when implemented with rigor and precision.
1. Super-Population Modeling: Predicting the Whole, Not Just the Parts
One of our core strategies was implementing super-population modeling—a statistical approach that uses auxiliary data (like voter files or census data) to predict how survey outcomes might look across the broader population.
This method addressed a major flaw in opt-in panels: self-selection bias. By modeling relationships between key variables such as age, voting history, and geography, we scaled predictions to reflect the full electorate rather than relying solely on the preferences of self-selectors. For instance, if younger respondents were underrepresented in our sample, super-population modeling helped us adjust for this gap by leveraging external data to “fill in” what the broader population’s responses would likely look like.
That said, this technique is not a magic wand. Its success hinges on the quality of auxiliary data and the validity of the underlying assumptions in the model. Nevertheless, even with these limitations, super-population modeling gave us a sharper lens for interpreting non-probability samples, bridging the gap between raw data and population-level insights.
2. Propensity Score Weighting: Balancing the Scales
Participation in online surveys isn’t uniform across demographic groups. For example, a politically engaged retiree in Florida may be far more likely to respond than a 22-year-old juggling school and part-time jobs.
To address this disparity, we employed propensity score weighting, which estimates the likelihood of survey participation based on traits like age, education, and geography. Respondents who were less likely to participate—but did—were “upweighted,” ensuring that underrepresented groups had a proportional influence on the final results. This method allowed us to correct uneven response rates, producing a sample that more closely mirrored the electorate.
Propensity weighting was particularly effective when combined with voter file data, as it gave us a robust framework to align responses with real-world turnout and demographic trends.
3. Calibration: Grounding Our Data in Reality
No matter how sophisticated the sample design, final estimates must be anchored in reality. That’s where voter file-based calibration played a critical role.
We adjusted survey weights to ensure that key metrics—such as voter turnout history, geographic distribution, and party affiliation—aligned with known population benchmarks. Calibration served as a final quality check, ensuring our models reflected the actual electorate rather than deviations caused by sampling biases.
For example, in states with polarized politics, demographic outliers like urban young voters or rural seniors could disproportionately affect survey results. Calibration allowed us to mitigate these imbalances, grounding our findings in observable, validated data.
4. The Power of Voter File Data
What truly set our methodology apart was the integration of voter file data at every stage. Unlike generic demographic quotas, voter files provided validated, real-world insights into the electorate, allowing us to account for geographic, behavioral, and demographic trends often missed by traditional non-probability samples.
For instance, voter history—such as whether someone consistently participates in midterms or only votes during presidential cycles—helped us identify likely voters far more accurately than self-reported intent, which is notoriously unreliable. By layering voter file data onto other techniques like propensity weighting, we produced results rooted in real-world voter behavior rather than theoretical assumptions.
Revisiting the Critiques
The Pew study rightly points out the limitations of opt-in panels. While our results weren’t flawless, layering techniques like propensity weighting, super-population modeling, and calibration narrowed error margins significantly. By the end of the 2024 election cycle, we emerged as one of the most accurate pollsters of the year. Across key Rust Belt states—Michigan, Pennsylvania, and Wisconsin—and with our final national poll conducted at the end of October, we achieved a combined error rate of just 1.1 points. These results demonstrated that non-probability polling, when implemented with advanced methodologies, can rival and even match the performance of industry-leading probability-based methods.
The Path Forward
Polling isn’t broken—it’s adapting. As traditional RDD methods face declining response rates and rising costs, non-probability methods offer a practical, scalable alternative. However, their success depends on innovation, transparency, and a willingness to embrace new tools.
Our 2024 experience demonstrated that when carefully applied, modern techniques like super-population modeling, propensity scoring, and voter file-based calibration can produce results that rival—or even exceed—the accuracy of older methods. These tools aren’t just stopgaps; they’re the future of public opinion research.
Polling’s evolution is inevitable. The question isn’t whether we abandon non-probability sampling—it’s how we perfect it.