So far in my University blog posts I’ve been writing about how it feels to be here. How I’m getting on with the culture and work of academic life after many years of doing the practical kinds of activities I usually write about on this blog. But I haven’t written about any of my academic work, until now.
This has been partly because I’ve spent most of the time since I arrived reading, listening, talking and generally learning. But I’ve started writing now as well, so thought I’d publish some of what I’m writing on here. Notes, thoughts and parts of academic essays. Partly because I’m delighted to be doing this learning. And partly because I’m interested in being able to explain what I’m learning, as I go along, to anyone who’s interested.
So here goes, and it’s about numbers.
‘When numbers get serious
They leave a mark on your door’
People like to see numbers, at the same time as being suspicious of them. Numbers to back up claims, to see what people would prefer, to substantiate our own beliefs. But most of us don’t understand how they’re arrived at and so will easily distrust and discount them. Or, ignorant of how they’ve been put together, assume we’re being fooled in some way? Which sometimes we are. So it’s complicated.
Here I am then, eleven weeks into an MA programme, illustrating the issue. Which is an issue with words as much as numbers. I like words, I’m used to them, I’ve been using them all my life and they’ve got me to most of the places I’ve been, including this university, while numbers have been occasional visitors. On tax returns, in barely grasped sets of accounts where I’ve relied on others to tell me if it all looks ok? And in a long ago Maths O-Level.
Since when I’ve mostly used words. To illustrate, convince, and engage people in my social issue of the day.
- Too many homeless people? “Yes, there are loads of them, all over the place, you must have seen them?”
- Too many empty homes? “Thousands, I bet there are one or more in the street where you live?”
- Slapdash, and if absolutely forced to quantify then I’ll still use mainly words to support such numbers as I’ve found to back up my own position.
- As in “Well the government admit to 200,000 empty homes in Britain, but Shelter say it’s over 300,000 and I know who I believe.”
It’s not good enough is it? Which is why I’ve been finding out more about quantitative research being done on the kinds of things I’ve been interested in all my life.
I’m grateful here to the first book about numbers I’ve ever read. In ‘The Tiger That Isn’t’ the authors talk about people like me, wondering why we only use half of our brains? Suggesting that since our numeracy is the other half of our literacy, we might well understand more about the things we talk, write and care about if we’d only use it? I can’t argue with that.
Gathering information in big numbers about us started with censuses of whole populations, once the industrial revolutions of the west had pushed us together in cities.
But it was in modern times, or at any rate the 1940s, that this quantitive gathering of statistics came more prominently to the general public’s attention, through public opinion surveys predicting election results in the United States.
C. Wright Mills ( a favourite sociologist of mine, I’ve mentioned him on here before) was famously dismissive of all this counting, calling its practitioners ‘research technicians’ rather than real social scientists. Despite this late 1950s opinion quantitative research has survived, as at least a useful tool for many though not all social scientists.
Never more obviously than during and after elections, where its use has seen much quantitative counting and analysis expand beyond the field of social science and become a tool of the popular media. This popularity has not, of course, arrived without some distrust of statisticians and their numbers. Though one of them, Nate Silver, has still become ‘the closest thing there is to a celebrity in the arcane field of statistical journalism’ through his accurate predictions of recent US and UK election results. (He does football too, if you’re interested?)
But predicting election outcomes is a relatively limited use of quantitative methods, compared with the British Election Study. This has now been running since 1964 and is the longest running social science survey in the UK. It’s what’s called a longitudinal survey, tracking the opinions of the same people over time. And one of the many thousands of things its research has found out is that there was not, in counted up numbers, a significant ‘youth quake’ vote in the 2017 General election to explain Labour’s strong showing. Even though my own observations had led me to go around saying I’d seen one.
I will write more about how all this kind of thing gets done and what else it might discover. But not just yet as I’m still learning.
I’ve learned enough so far though to know that counting things sometimes counts for something. And that having strong opinions based only on our own individual observations or preferences is, more than sometimes, not good enough.
Which is kind of the point of me studying stuff like this, as well as all the more word based sociology and history I’m more naturally at home with.
But more of that another time, in ‘Serious Numbers/2?’ And perhaps some actual numbers?
You can read more of my University writing here.
Much of what is written here, by the way, has now become part of a ‘real’ academic essay. The fact being that I don’t intend to divide my writing into ‘blog’ and ‘not blog.’ Because I want what I write to be understandable by anyone who cares to read it. And because I’ve been thinking a lot about the craft of writing itself. But that’s for another blog post, coming soon.