Data of the People: Methodical Approaches to Uganda's 2026 Elections

As Uganda prepares for its general elections scheduled for January 2026, the nation stands at a crossroads where data science could either strengthen democratic institutions or deepen existing divisions. The difference lies not in the abundance of numbers, but in the rigour with which data is collected, analysed, and interpreted. In an electoral environment where 409 polling stations reported 100% voter turnout in 2021, methodical approaches to data analysis have never been more critical.

Why Data Matters This Season

Uganda's electoral landscape presents unique opportunities for data-driven insights. The country has developed substantial statistical infrastructure through the Uganda Bureau of Statistics (UBOS) and maintains comprehensive electoral rolls through its Electoral Commission. Yet the challenge is not accessing numbers; it's applying rigorous methodology to transform raw data into reliable insights that can strengthen democratic processes.

The stakes are particularly high given Uganda's electoral history. President Museveni won 59% of the presidential vote in the last election, with his National Resistance Movement party securing a parliamentary majority. However, opposition challenges and international scrutiny have highlighted the need for greater transparency in electoral analysis. Data can serve as a neutral arbiter, but only when handled with statistical discipline.

Without careful methodological approaches, data can mislead instead of illuminate. Selection bias and inaccurate causal inferences can turn well-intentioned analysis into propaganda tools. The antidote is not to avoid data, but to embrace methodological rigor that makes assumptions explicit and limitations transparent.

Data for Prediction and Planning

Electoral forecasting – predicting who will win on a local to national scale – in Uganda requires navigating complex demographic and geographical realities. Historical election results combined with census data – Figure 1 –, survey responses, and economic indicators can power sophisticated predictive models. However, success depends on avoiding systematic biases that have plagued polling in emerging democracies.

Figure 1: Vote Share by Candidate at the District Level (2021). Source

Selection bias represents perhaps the greatest threat to accurate forecasting. If survey samples overrepresent urban, educated, or mobile phone-owning populations, predictions will systematically misrepresent rural Uganda, where agriculture employs over 70% of the population. Recent reporting indicates rising concerns related to civil liberties vary significantly by region, with urban areas reporting issues like social media censorship while rural districts face challenges like access to polling stations.

Data for Understanding Behaviour

Moving beyond correlation to causation requires explicit theoretical frameworks. Causal graphical models (CGMs) provide a structured approach to mapping relationships between voter characteristics, policy preferences, and electoral choices. Rather than simply observing that education correlates with voting patterns, CGMs force analysts to specify causal pathways: Does education directly influence vote choice, or does it work through intermediate variables like political awareness, media consumption, or social networks?

In Uganda's context, spatial autocorrelation methods can significantly enhance the reliability of CGMs by accounting for geographical clustering of political behaviour. In 2021 for instance, Robert Kyagulanyi aka Bobi Wine led in the Central region and metropolitan area, while current president Yoweri Kaguta Museveni led in the Northeast and Southwest regions – Figure 1. Such voting patterns rarely occur in isolation: neighbouring districts often exhibit similar preferences due to shared ethnic identities, economic conditions, or historical experiences. Spatial methods help distinguish whether this reflects genuine regional preferences or spillover effects from influential neighbouring constituencies.

Consider a spatially aware causal model for Ugandan voter behaviour:

• Education level → Political awareness → Media consumption → Voting turnout (moderated by district-level education rates)

• Income level → Policy preferences → Candidate evaluations → Vote choice (influenced by regional economic conditions)

• Regional identity → Issue priorities → Party affiliation → Electoral participation (accounting for ethnic geography and historical voting patterns)

These models make explicit assumptions that can be tested and refined through both statistical analysis and spatial diagnostics. They distinguish between confounders – factors that influence both cause and effect – and true causal drivers while controlling for the reality that political preferences cluster geographically. With access to readily available longitudinal data, the models can then be validated or improved retrospectively by identifying factors that persist regardless of election season. It is worth noting that they are only informative, not decisive, because ‘all models are wrong but some are useful’ as the late statistician George Box famously put it.

The CGM framework transforms causal inference from a buzzword to analytical tool that respects Uganda's geographical complexity. Instead of simply claiming that ‘youth support opposition parties’, methodical analysis incorporating spatial methods might reveal that youth employment status, educational attainment, and urban residence create different causal pathways to political preferences per region or other demographic stratifiers.

Data for Transparency and Accountability

Statistical methods can serve as powerful accountability tools when applied systematically to electoral data. Benford's Law offers one such approach, examining whether vote tallies follow expected digit distribution patterns. While academic research has debated its effectiveness for election fraud detection, it is highlighted as a screening tool rather than definitive proof.

The law predicts that in naturally occurring datasets, the leading digit ‘1’ appears approximately 30% of the time, ‘2’ appears about 18% of the time, and so forth. Significant deviations from this pattern can flag potentially problematic data requiring further investigation. However, Benford's Law works best with large datasets spanning several orders of magnitude characteristics that may not apply to voters from small and sparsely populated districts.

Web scraping technologies enable real-time monitoring of campaign promises, media coverage, and public sentiment. With appropriate ethical safeguards and respect for privacy, systematic data collection from social media platforms, news websites, and government portals can provide triangulated information sources. This becomes particularly valuable in environments where official data may be delayed or incomplete but still falls prey to governmental control of media.

A noteworthy effort by the International Republican Institute (IRI) was made to make electoral data since 2006 more available to the general public through the Uganda Elections Data Portal. Their stated mission is to increase transparency and understanding of voter registration and results made publicly available. With the tools discussed so far, a Ugandan citizen or interested person can access historical data published by the Electoral Commission and use it to inform current and future decisions on an individual, community or institutional level.

The Ugandan Data Context

Uganda's data landscape presents both opportunities and challenges for electoral analysis. The country maintains substantial statistical infrastructure through the Uganda Bureau of Statistics (UBOS), which conducts regular household surveys, census operations, and economic monitoring. The Uganda Data Portal provides comprehensive datasets on elections with visualizations and analysis tools. Electoral rolls managed by the Electoral Commission provide baseline demographic information, though questions remain about completeness and accuracy in remote areas. NGO monitoring organizations contribute additional datasets on voter education, campaign finance, and electoral violence. The expansion of mobile phone networks and mobile money services creates new data streams about economic behaviour and social connectivity patterns.

However, significant challenges persist. Rural coverage remains incomplete for many data collection efforts, creating systematic gaps in understanding of Uganda's predominantly agricultural population. Political sensitivities around data collection can affect survey response rates and data quality, especially in extreme cases where there is risk of death or imprisonment. Additionally, the digital divide means that social media and online data sources may not represent broader population views.

The opportunity lies in combining multiple imperfect data sources with transparent and methodological approaches. Even noisy data becomes powerful when limitations are acknowledged, sampling procedures are documented, and analytical methods are shared openly.

Conclusion

Uganda's 2026 elections will not be made fair by numbers alone. What matters is whether the country's analysts, journalists, civil organizations, and political actors embrace methodical approaches to data analysis that prioritize transparency over partisanship and evidence over speculation.

This means moving beyond superficial statistics toward rigorous methodology: explicit causal models that make assumptions transparent rather than hidden; longitudinal tracking which reveals how attitudes evolve over time; systematic awareness of selection bias in survey design and interpretation; careful application of statistical auditing tools like Benford's Law as screening mechanisms rather than definitive judgments; responsible use of web scraping and open data sharing that respects privacy while promoting accountability.

The technical infrastructure exists. The Electoral Commission has established roadmaps and timelines for the 2025/2026 general elections, providing a framework for systematic data collection and analysis. Civil organizations maintain monitoring capacity, academic institutions and advisory firms possess analytical expertise, and international partners offer technical support. What remains is the political will to prioritize methodological rigor over convenient narratives. Uganda's electoral season could shift from rumour and speculation toward evidence and accountability, provided that data is treated as a tool for understanding rather than a weapon for political combat.

The path forward requires humility about what data can and cannot reveal, transparency about methodological choices and limitations, and commitment to letting evidence guide conclusions rather than forcing data to support predetermined positions. In an electoral environment where trust in institutions remains fragile, methodical approaches to data analysis offer a pathway toward more informed democratic discourse.