chapter 8 Talking different languages of value
Talking different languages of value
This chapter began its life in a Manchester hotel in 2014. I was preparing a conference presentation called ‘Measuring National Well-being and Cultural Participation—why don’t things quite add up?’ A colleague was passing as I was editing a slide with this equation on it:
He asked me what the equation was for. I was a bit taken aback, because I had assumed that while I didn’t really understand what the equation was saying, that this would be immediately obvious somehow to people who work in quantitative methods, or ‘Quants’. I explained that it was from a report on measuring happiness for the cultural sector, but that I didn’t think that it would mean much to many I knew. As I outlined in this book’s Preface, I had experienced a general lack of data confidence in the cultural sector and I imagined that most people reading a report called ‘Museums and Happiness: The Value of Participating in Museums and the Arts’ would struggle to make sense of the equation. In some ways, more importantly, that this equation was probably a barrier to understanding data and these valuations more generally. My colleague joked that he wasn’t sure it was talking his language either, and agreed it probably wouldn’t make much sense to the sector.
I left the conversation with one overarching question: what does an equation like this do in this context? How does it reinforce the divide between those who see value in valuations, and those in cultural and social sectors, or people working for small charities, who maybe do not? Or perhaps, aren’t sure? Could the ways that ‘quants’ are presented reproduce traditions called ‘the Quant-Qual debate’ that we touched on in Chap. 3, even outside research contexts? Is this detrimental to the ways that some people feel capable of actually reading the research reports that evidence arguments they use in their day-to-day jobs in the cultural sector? This equation triggered more questions for me and my colleague: Who was this algebraic expression for and what was it aiming to do? How did the
equation relate to the headline findings, and most importantly, whose value and values might be expressed in such a way?
I wondered if we could ‘follow the data’ to answer some of these questions about the equation. Which we did—in our own different ways. I mostly handled the qualitative research: I looked at the report on museums and happiness, and the cultural, policy and data histories that preceded it. This work contextualised the report in various ways, enabling us to see how it ‘fit’ in the general overlapping concerns of data, well-being, politics and value that we have encountered throughout this book. More specifically, these include beliefs and theories about well-being and its role in society, ambitions of the movement to establish cultural value, developments in well-being metrics, which coincided with a desire for valuation from government—and questions of data’s capability remaining unanswered.
‘Following the data’ also included ‘following the findings’. In other words, understanding context also meant researching what came after the report and how its findings were used. This gave us an idea of the impact of the report, and how it was received by different audiences. Following the findings also included reproducing aspects of the original research. My colleague led on this, the quantitative side of our project. This chapter walks you through steps in the original research about museums and happiness, as well as our subsequent project to demystify what is going on in these kinds of valuations.
Earlier in this book, I covered some of the discussions about how data tend to be presented as these neutral and objective things. This means that in some cases, it should be possible to do the same thing with the same data and arrive at the same results. This is one of the reasons why there are so many ‘workings’ in quantitative research—including the equation we started with: this working out is presented, so it can be scrutinised, and potentially reproduced. This is also one of the reasons why quantitative approaches are often thought to be more persuasive and robust than qualitative ones. It is not necessarily that numbers are more powerful in and of themselves; rather it is assumed that less interpretation is undertaken by the researcher. Therefore, you can work towards reproducing someone’s findings by following their steps in quantitative research, in a way that you would be unlikely to do in qualitative research.
This happenstance discussion about an equation in a Manchester hotel in 2014 led us to a project that wanted to understand the value of this genre of research to the cultural sector—and beyond to charities and other areas of social policy. Were there limits to understanding and presenting the culture–well-being relationship in this way? What would happen if we followed the data and processes used ourselves? The headline finding of our project is that when we reproduced these processes, we had a different finding: the monetary estimates of the relationship between participation and subjective well-being do not match across our reading and the original. Why might this be?
There are a number of reasons why two pieces of research following the same steps with the same data might offer different results. We will return to this later, but first, the aim of this chapter is to ‘follow the data’ on a journey of informed discovery, hoping to achieve a number of other things along the way. First, break down some of the barriers between quantitative research that helps people with their advocacy, and the practitioners who will read it and need it. Second, enable people to feel more confident with quantitative expressions and some of the language and principles of quantitative research. Third, it is a reference for people to return to, and apply to other reports they need to understand, but which ‘do not speak their language’. This leads me to fourth, to help people feel greater data confidence and literacy, and perhaps enable them to make better judgements for themselves about whether more than headline findings can be useful— to them or in general.
- I have since learnt that actually there are differences in the ways that different disciplines express characters in equations; and so, arguably they also talk different languages in this way, but we don’t need to get into marginal differences between economists and statisticians here.
- It is really hard speaking for him. If truth be told, I am not sure I knew what he was thinking, exactly, five years ago.
- Many qualitative researchers argue that the value of context, bias and subjectivity is too important to qualitative research to enable it to be reproduced in a way that findings could be repeated.