Stories behind statistics
Whenever you want to research something, there are two basic choices:
- Collect quantitative information through monitoring or with surveys, where many respondents provide simple answers (checkboxes, ranking scores, etc).
- Collect qualitative information through interviews, where a smaller number of respondents provide detailed, context-rich answers to open questions.
Both methods have very clear advantages and disadvantages:
Quantitative information is easy to gather and process and it provides nice statistics.
But do we actually gain insights? Do we understand the complex experiences of our target population any better if we only ask them to report simple facts that we feel are relevant, followed by our own analysis and interpretation?
Qualitative information takes more effort to gather, and usually involves smaller numbers of respondents. But faced with several pages of text for each interview, how do we summarise results? How do we find patterns in such a huge amount of information? And if we report findings, how objective are the results that we report?
The point is this:
Numbers without stories to back them up are objective, but not persuasive. Stories without numbers to back them up are persuasive, but not objective. We need to combine both types of data and do it simultaneously, so that there’s no risk of results from the first step feeding into assumptions on the next step.
SenseMaker® delivers qualitative data and quantitative data simultaneously
From cognitive science, we know that human brains actually prefer large amounts of fragmented information to understand reality. As humans, we like messy coherence.
This is why our software collects small stories (one or two paragraphs each) in either written or spoken form. Photographs and drawings also work. The more fragments, the better – because every story is told from a different perspective. We want as many different perspectives as possible!
But how do we make sense of these vast amounts of fragmented narratives and pictures? If we let experts interpret the data, we introduce unwanted filters and bias.
The answer is to let respondents speak for themselves: they know what they feel is significant about their story or picture, so we ask them to index (signify) it. This way, respondents also act as experts by interpreting their own story.
The result of this process is qualitative information, indexed by the sources so that quantitative data emerges, without any external interpretation!
SenseMaker® lets decision makers drill down from broad patterns to single stories
The huge benefit from having no intermediate layer between the raw stories and their interpretation is that decision makers can look at quantitative patterns first, and then go straight to looking at the narratives or pictures that make up these patterns.
This allows decision makers to make sense of the quantitative trends by looking at the underlying qualitative data. Of course, the narrative can also be used in communication, serving as a powerful illustration of key insights or project results.
Because we can also ask simple multiple-choice questions to categorise stories or respondents, we can provide all kinds of statistics. We can filter and categorise stories to contrast and compare groups, attitudes, intentions – all from the same data and without any ‘experts’ involved.