So, how do you go about analysing that?
As most people visiting this site will know, I’m doing a research project about what is helpful and unhelpful for people after a disaster. The way that I am asking people to participate is by writing a letter to themselves about that topic.
One of the questions that I get asked when describing this process is ‘yeah, but how do you compare them? You can’t really because you’ve got apples and oranges because everyone will write different things in their letters.’
In some ways this is right, but in other ways it’s not such an issue. I deliberately left the question very open – What has been helpful and unhelpful when recovering from a disaster? The very cool thing about the question being so open is that it doesn’t lead people to talk about one thing or another, they talk about what’s important to them, and that is very, very important to me. The trickier bit is finding a way to consistently review the information that people send in to see where there are commonalities and differences.
To start, it’s important to remember that this is a qualitative research study, so I’m not trying to create statistics from the responses, or be able to make ‘rules’ that you can then apply to anyone. Qualitative research often uses what’s called ‘inductive’ reasoning (looking at what the data is telling you, and then trying to find a way to organise that), compared to quantitative research which often uses ‘deductive’ reasoning – that is, start with a hypothesis and test examples against it. Sometimes deductive reasoning is called ‘bottom up’ research and inductive reasoning is called ‘top down’. Both qualitative and quantitative research are important for different reasons.
The University of Wisconsin in the USA has a simple table on their website that might be useful to help break down the differences if you’re interested in learning more about the differences between quantitative and qualitative research.
The theory that I’m using in my analysis fits under the category of ‘Grounded Theory’. Grounded theory came out of the USA in the late 1960’s by two academics, Glaser and Strauss and is essentially a structured way of looking at social research data with the intention of developing theory from it.
There are loads and loads (and loads and loads…) of different interpretations of exactly how to use grounded theory. I kid you not, there are not only books, but entire libraries, websites and institutions dedicated to looking at the different aspects of grounded theory. I am using a version of it as explained by Professor Kathy Charmaz (a Sociology Professor from the USA). She calls it ‘constructivist grounded theory’.
While there are critics of this approach (because almost every approach has limitations), some of the things I really like about it are:
· It really puts the research participants in the drivers seat
· It encourages researchers to look at their own biases, and instead of hiding them, name them
· It really encourages researchers to be analytical rather than just descriptive
The process that Charmaz advocates uses ‘line by line’ coding, where the researcher looks at the different sentences in an interview or document (in this case, letter) and starts building an analysis from there.
It sounds weird when you first get introduced to this process, but very quickly it lets you see some trends that are going on, and lets you see them a bit more meaningfully than a list of ‘things’.
There is a really helpful (but quite long) interview with Professor Charmaz by an academic in the UK here if you’re interested in more on this, you can watch it here (it goes for about an hour).
I’m not sure if this is a helpful summary, but hopefully it gives you a bit of an overview about the approach I’m taking. If you’d like more information, drop me a line.
As always, if you’re keen to participate in the research, or know someone else who might be, I’d love to hear from you. You can click here for more information, or email me at email@example.com
I’m still hoping to receive letters about what people have found helpful and unhelpful after a disaster up until June 2017.