Redefining Research
While our first public research releases are currently in development and findings will be published here after careful analysis, I’d like to first introduce you to the story behind why this work exists.
I started Axiom Analytics because I believe we need to do better research. Research that asks better questions, captures nuance, and reflects the real complexity inside people’s beliefs instead of skimming the surface.
The moment that crystallized this for me came when a well-known public opinion research firm published a report which sought to describe groups of voters based on political affiliation and demographics. The firm was reputable, and the data collection itself was solid, but the way the categories were constructed and interpreted revealed a deeper problem in how people we measured.
The best example of this was in the way education was grouped and weighted. I noticed that a Red Seal trades certification, which takes years of training and apprenticeship, didn’t seem to be given the same recognition to post secondary academic pathways. At the same time, people without completed post-secondary credentials were implicitly treated as a single lower category. This without recognizing that many are entrepreneurs, inventors, and builders, are the very people who form the backbone of our economy. When categories are too blunt, they quietly erase those who’ve taken the risk to be innovative, their contributions, and lived experience.
What also stood out was the tone used to describe the findings. There was a subtle but noticeable sense of othering where the language used positioned one set of beliefs as outside of the social norm by saying “this group believes this, while everyone else believes that.” That kind of framing can create social distance and implied hierarchy, turning analysis into narrative and nudging readers toward judgment instead of understanding.
Another major issue is how often some surveys rely on agree/disagree questions for statements that actually contain multiple ideas. Some issues are too complex to compress into a single sentence response. When large concepts are bundled together, we end up measuring reactions to wording instead of structure of belief. The example I noticed was most evident with questions around environmental protection and climate policy. For some people, those are terms are interchangeable and represent a single belief. For others, they are related but distinct priorities, with feelings that may produce separate responses when separated. If the questions we chose to ask merge these kind of large complex ideas, we lose the nuance of understanding of where exactly opinions differ, for whom, and why.
The heart of this work is to pull complex issues apart instead of collapsing them. To design questions that explore tension, tradeoffs, and conditional views. To recognize that people don’t hold belief systems in neat, ideological packages that fall perfectly along party lines. Most people carry mixed priorities, competing values, and context-dependent judgments.
Our goal is research that treats that complexity with respect. To understand beliefs, values, challenges, and opportunities across the population and to present findings as clearly and as free of bias as possible.
Because better questions and greater transparency, lead to better solutions, better decisions, and richer conversations.
