The following examples were taken from interviews with stroke survivors
(Easton, 2001). The examples provide some raw data from which you can derive
some ideas or common threads. In the first example, what do you perceive? Are
any important concepts, ideas, or themes expressed? What was the participant
trying to convey?
Well I know one of the therapists, physical therapists here at the campus, uh,
I talked to her about it and then she says, oh, she says, it may never get all
right. It was sorta a jolt for me. (p. 148)
Compare the next example with the previous and see whether you notice any
similarities or differences. If so, what are they? Are the different participants
in these interviews saying anything that might lead you to believe they had
similar experiences?
That, uh, you know, just get up and go do what you were gonna do. But that was
just a flashing—and then we realized that, uh, we can’t do that no more. (p. 148)
Look at one more quote from yet another stroke survivor. Are there any
similarities between this participant’s ideas and those of the other two cited
previously? If so, what are they?
I was home probably, oh, I had been to the SOS meeting, and after listen-
ing and watching some of the people there, I realized I may never get back
everything. (p. 148)
In the previous examples, one might observe some common threads such as
a sense of loss or realizing that things may not be the same as before the stroke.
These snippets of data are examples of those that researchers might identify
as containing codes or ideas. Using the ideas found within the data as codes,
researchers compile lists of codes and then return to the data again and again to
categorize data into organized units. Remember that analysis occurs while data
collection is still in progress. When common ideas begin to emerge, the analysis
should be used to adapt future interviews and direct and focus the data collection
process. This entire process of data analysis and interpretation can be compared
to discovering and putting together the pieces of a puzzle to form a meaning-
ful picture that best represents the essence of the story told by the participants.
Sandelowski (1995) recommended some alternative strategies to traditional
coding for developing a system to manage the data. She suggested extracting the
facts and looking for story lines, topics, and content in addition to reduction
of the data. Facts are those items of data that are not subject to debate, that are
“givens,” and later may be important for establishing the context of the research.
While capturing the data in a systematic way, Sandelowski’s approach avoids
line-by-line coding. The aim is to think of the data as telling a story. Research-
ers approach the data by trying to identify the main points of the story or the
major idea. Sandelowski suggested making a list that captures all the topics
present in the data, much like the major plot points in each scene of a play.382 CHAPTER 14 What Do the Qualitative Data Mean?