REVIEW article
Front. Polit. Sci.
Sec. Political Science Methodologies
Volume 7 - 2025 | doi: 10.3389/fpos.2025.1592589
This article is part of the Research TopicMethods in political science – Innovation & DevelopmentsView all 7 articles
Making Online Polls More Accurate: Statistical Methods Explained
Provisionally accepted
- University of Padua, Padua, Italy
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Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels, and even comments scraped from the internet can offer valuable insights into political preferences. However, such data is often affected by significant selection bias, as online respondents may not be representative of the overall population. At the same time, traditional data collection methods are becoming increasingly cost-prohibitive. In this scenario, scientists need instruments to be able to draw the most accurate estimate possible from samples drawn online. This paper provides an introduction to key statistical methods for mitigating bias and improving inference in such cases, with a focus on electoral polling. Specifically, it presents the main statistical techniques, categorized into weighting, modeling, and other approaches. It also offers practical recommendations for drawing estimates with measures of uncertainty. Designed for both researchers and industry practitioners, this introduction takes a hands-on approach, with code available for implementing the main methods.
Keywords: review, Non-probability samples, Non-ignorable selection, electoral polling, Missingness, MRP
Received: 12 Mar 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Arletti, Tanturri and Paccagnella. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alberto Arletti, University of Padua, Padua, Italy
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