Random Notes on Research: From Bigfoot to Einstein via Wiki

Upali Nanda, PhD, Associate AIA, EDAC, ACHE

Original post from Upali Nanda on LinkedIn.


Research is in. You can find no industry today that does not celebrate research and talk data and numbers. Unfortunately, it has also become a post-rationalization tool - using data and numbers to justify a decision and push forward a point of view. In a world where information abounds, and technology is rampant, you only need to google the right words to get an answer that satisfies you, and you can then cite. Nowhere is this penchant for wielding data with inherent bias more evident than in the ongoing election campaign. I almost cringe to be a researcher when I see "research" spouted as a series of poisonous darts to sway public opinion. A couple of years ago I had an interesting dialogue with colleague Jason Schroer on bias- that came about from his questions on reality TV show Finding Bigfoot- and how those who believe in Bigfoot, always landed up finding proof he existed: http://www.hksinc.com/insight/the-bigfoot-conundrum-a-practitioners-perspective-2/; Till date, in my mind, that conversation on dealing with bias is filed as the Bigfoot Conundrum.

If we go back to the basics, research is defined in the dictionary as "the systematic investigation into and study of materials and sources in order to establish facts and reach new conclusions". The focus is really on the "systematic", and on the choice of relevant materials and sources. This, in turn, depends on two key elements- the clarity of the research question, and the adoption of the appropriate method to seek the answer. We are seduced too quickly by numbers, especially when they are big and pretty, but stopping to think about the genesis of those numbers- where did they come from, what question did the research ask, and what method did they use to find the answer is key. And once we know that, knowing the bias, (or what we now call the bigfoot belief :), is imperative. That constant unfailing acknowledgment of bias (because you can never truly achieve lack of bias) is fundamental to good research.

And because bias exists, and is un-avoidable, we as a generation of information consumers, have to stop this blind belief in numbers. Not all good research is quantitative. Not all metrics are meaningful. And the presence of numbers is not proof. But "if" we want proof, we must understand clearly the notion of a hypothesis.

At the high tide of evidence-based design, there was a rush to prove how design improved outcomes. In itself this is a powerful idea- because design can, and does, improve outcomes. But it rarely does so in isolation of people, and process. In other words, in the context of architecture, determining how our built environments can "cause" improvement in key outcomes (satisfaction, safety, loyalty, productivity etc. etc.) is very, very tricky. Informed by many dinner table conversations by my in-house stats professor, I wrote this piece that followed the bias (read bigfoot) conversation, delving into how tricky this conversation could get, the importance of the "null" hypothesis, and why we must remember that correlations are not causality, and the purpose of research is to investigate, not prove. The outcome of good research could be the lack of proof. http://www.healthcaredesignmagazine.com/article/wrong-way-conduct-ebd-research?page=2. Again, it comes down to those three key elements, a good question (Q), a sound method (M), and acknowledgment, and minimization, of bias (B). But the most important of the three, in my opinion, is a good question. I love this quote by Einstein:

"If I had an hour to solve a problem, I would spend 55 minutes thinking about the problem and 5 minutes thinking about the solution". THAT, there, is the key to good research. You have to spend enough time thinking about a problem, so you can devise the right research question. Without a good research question, all you will have is data & information, but not insight and knowledge.

We over-complicate research, and we over use the term. In the end this wiki definition of research is perhaps the one that aligns most with our design industry:

"Creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of humans, culture and society, and the use of this stock of knowledge to devise new applications."

All too often we use research to peddle ideas, sell opinions, and justify decisions, while what it really does (or should do) is provide us an opportunity to learn better, so we can DO better. Perhaps the best thing about being a researcher in practice, is the ability to witness that transition for learning to doing. And then learning from what was done, so we don't come full circle, but spiral forward towards meaningful change.