-: Welcome to the video lecture
on qualitative data analysis
where we will learn, once we've collected data,
how do we analyze it, how do we make sense of it
to turn the raw data into results?
So we're gonna do a quick introduction
to talk about the basic steps of the analysis
and the specific method called coding,
and then revisit the idea of how can the researcher
demonstrate rigor in the process
to make sure that the results are credible
to the readers.
So basically, the definition is to
discover underlying meaning in non-numerical data.
Note that this tends to be more art than science,
that there's many right ways of doing it.
It would be unusual that two researchers
or more would do it in exactly the same way,
but if done well,
it can nonetheless be credible and useful.
And note that it's also highly iterative,
that you're always going back and reassessing
and looking at it through new lenses
as they emerge.
So here are the basic five steps,
and we'll walk through each one.
So first, you want to document the data.
So this involves, you go and you do whatever it is,
the interview or the observation.
You often will record it and transcribe it.
And you also wanna take field notes, and you wanna go
and review and reorganize these field groups.
And by transcribing, you have a word for word account
of what the interview subject said.
Next, you wanna start to organize them into concepts.
And the best way to do this is just to spend some time
with your data, reread your transcripts, reread your notes,
make notes to yourself on themes and ideas
and connection that you notice.
And this can be done both one question at a time.
So you look at all of the responses
to question one from all of your respondents,
or you look at all of the first respondents,
then the second.
And really, both of these two ways work.
And then you code them, you name the patterns
that you perceive,
and you start to sort of organize these concepts
into these code to sort of give them a meaningful structure.
Some of these codes may be preset based on the questions
that you ask, but many of them will
and should be emergent, you know, sort of themes
that you would not have thought of,
but that emerge from the data.
And in a few minutes,
I'm gonna talk about the specific mechanics of coding.
So when you create these codes, in many cases,
you want to categorize them into sort of classes,
something like present or absent,
something they said or they didn't say,
or high, medium, low, a strong belief,
a medium belief, a low one,
or sort of the intensity of a feeling
or an awareness.
And it's very, again,
no two researchers will maybe do this exactly the same way,
but it's important for you to be clear
of where are the boundaries of these boxes?
How did you sort into high, medium, low, and why?
What are the boundaries that, well, what make you say
that this was high intensity and this one is low?
And make sure that you document it as you go.
Next, you start to look at
connections within the data.
And I'm going to use an analysis
or use, sorry, an analogy to
statistical analysis.
So the first way is looking at these themes.
So you can think about creating a spreadsheet
where the columns are the themes
and the rows are the respondents.
And within that column
and row is a little snippet
of the interview, which fits underneath that theme,
which was spoken by that respondent.
And on your first go round,
you're just sort of describing what are those themes
and how often can they occur?
And then in the second
more analogous to bivaraite analysis,
is there a relationship between how someone,
those who was say, high, medium, low,
or a certain theme to another theme.
So again, you can list like
what were the themes,
how often were they mentioned?
And it's good to select one or two quotations,
which really sort of epitomize
or embody those themes.
And again, this is analogous
to writing out the descriptive statistics,
like a frequency analysis with quantitative data.
And then once you've made this data matrix,
you can see are there relationships between
theme X and theme Y.
Sort of like, and one from the past
is in a previous version of this class
is we saw a relationship between,
between one's class standing
and the intensity of environmental values
and the use of reusable water bottles
that we found that there's
sort of a UVM effect
and this is maybe before environmental values
were as widespread as they are now,
but students who had been here longer
tend to sort of learn about and embrace
and hold stronger environmental values.
And one of the things that they talked about
how they behave based on these values is using a reusable
water bottle in instead of buying a throw away
plastic one each time.
But for any of these themes,
are there recurring patterns that someone who is high
or medium in one will also be
higher, medium or low, et cetera
in the next.
Next, you want to corroborate, you want to sort of
compare the meaning that you have discovered
with other contexts.
So you think about why did they say that?
Are there other data sets or contexts or theories
or results that sort of speak to this?
Are there other reasons why they might be saying it?
And here's a place where you really wanna be reflexive
and make sure to examine
and try to take into account your own biases
and why did you see this the way that you did?
Are there other ways that other researchers
who aren't you might have seen it and why?
Then you want to report out.
So you wanna be very clear about why did you name the codes
the way that you did and those relationships.
And again, that's why sort of memoing to yourself
really helps you to be able to clearly say,
I put this quotation into this
under this theme,
and I saw this relationship between these themes
and explained why you made that decision that you did.
It's a good idea again, I had said how it's good to find
a couple quotations per important theme,
which really speak to it.
And the reason that you do is it
provides specific evidence
and then avoids the, sort of take my word for it,
sort of what's a response
that was commonly said.
Was there a range of what folks said?
And you also wanna explain why did you
choose that quotation?
And finally, you wanna allow for enough of the quotation
to set some context.
But you always want to respect confidentiality,
that you don't wanna give
so much information that the reader can attribute
the quotation to any individual.
Because in the most cases,
when you do the IRB process
and you talk to the subject
about the research process,
part of the agreement that you make
is that you will not at attribute any specific
quotation or answer to any individual.
So you want to be careful that you have enough
to set the context so the reader understands,
but not so much that you're identifying who said it.
Finally, when you report, you wanna talk about shortfalls,
you wanna talk about the lack of generalizability,
any biases of yourself that you might have.
And finally, you wanted to talk about what future questions
and directions do these results suggest.
And here are some more good resources to read up on that.
So now I'm gonna talk about the actual process of coding,
of how do you name and organize bits of data
from your collection process
such as interviews.
So you can think of it as that you're taking
little pieces of paper, so you don't actually do this,
but conceptually you take quotations
and you sort of cut them out,
and then you put them into
little folders organized
by common themes.
And again, this is a very inductive process
where you use the data to come up with these themes.
It isn't that you start with a theory,
you start with the data and therefore it is induction.
So you can think of the themes being like a folder,
and a folder has a name on it,
the name of the code,
and you put sort of these statements
that fall under that theme,
that sort of articulate
a common meaning.
You put it under these, you conceptually put them together
in these folders.
This coding process happens in three steps,
open, axial, and selective.
And one way that I like to conceptualize
is that the open codes are like the file folders.
The axial codes are like the file cabinet drawers.
And the selective codes is the overall file cabinet.
I think you'll see what I mean in a second.
So open codes are these folders,
and that's the first step of what are the emergent themes
that arise in your first read through.
And they are determined
by the researchers examining of the data
where you uncover name and expose the meaning.
And at the end you'll probably have
depending on the length of and number of interviews,
there'll be a lot of them,
maybe let's say 40 for them, where there's a long list.
They're not really sorted
by any frequency or importance.
Just sort of a long list of themes
that you notice in your first few read throughs of the data.
Next is the axial code,
and this is the secondary analysis
where you think about how do your codes fit into groups.
So you sort of code your codes where you pair down
and you sort of put like with like,
and think about and come down to maybe five axial codes
of what are five sort of next level
more comprehensive codes that these say 40
open codes fit into.
And this is like the file cabinet drawer
in which these various folders fit in.
And last is selective coding,
where you try to think one or two
of the most important concepts
that really sort of sum up the meaning of your data.
And this is analogous to the overall file cabinet.
In this way you sort of create a tree
where the open codes are on top
and you see how that they fit into various secondary codes.
And then one overarching code at the bottom.
As I said before, it's really important
as you do the coding, write memos to yourself.
Remind yourself what you did and why.
Why did you call this what you did?
Why did you make the decisions?
Why did you put this quotation under this code?
And if you have any sort of theoretical ties
and such, again, put all those into a memo.
So here again is the evidence of rigor.
And I'm gonna talk to these bottom ones here,
especially where you want your codes to make sense,
to be transparent and you want multiple.
You often wanna use multiple coders
who do the coding independently
and then see if they sort of came up with the same thing.
And if so, that makes your data more reliable.
And when you report out, again,
being transparent about what you did and why,
and that the voice of the respondents
should come through, and in many cases,
due to really good quotations that you choose,
which sum up these codes.
Here are five more ideas.
This is a really good article here
about how can you show and what should you think about
for the trustworthiness of your data?
How do you show the analysis that you did
and the way you did about what about it
that the reader can trust that it was done well
and can sort of believe what you said.
As with most other social science research,
you want to think of ethical issues.
Did you only find what you thought that you would?
Did your confirmation bias
overwhelm all other things?
Are you clear about, were you reflexive
and say, well, I saw this but I could, you know,
but that's because of the lens I bring
and here are other ways that it may be interpreted.
And then again, you don't want to attribute
any quotation to any individual,
but you may wish to just sort of track
how an individual answers a number of questions.
So in this case, something like a pseudonym would, you know,
call 'em a name, make up a name,
or even a number or A, B, C, D.
But that allows you to sort of say,
show how respondent A talked about this and this and this
and that may be an important thread.
And here again, are the key takeaways.
Thank you for watching.