Qualitative_data_analysis

Chapter 12
Making connections
Imagine a snapshot of an athlete clearing a hurdle. What can we tell from our
snapshot about what is happening? Although we have only a static image, we will
have some idea of what has just happened, and what is going to happen. We may
not understand the law of gravity, but we do have sufficient grounds for inferring that
what goes up must come down. All the same, we would be happier with a succession
of images which provide more direct evidence of what is going on. A videotape
would do nicely. Then we would be in a much better position to answer the
question ‘what happens next?’ Qualitative data often provides us with just this sort
of direct evidence about the dynamics of what is happening.
How can we analyse these dynamics? One way involves analysing data into
categories which capture the main elements of social action, and then noticing and
documenting how these categories interconnect. For example, suppose we have
three successive images of our athlete. In the first, the athlete is poised to jump or
has just sprung into the air. In the second, the hurdle is cleared. And in the third, the
athlete has landed on the other side. We could categorize these as three actions—
jumping, clearing, and landing. Suppose we find these actions tend to recur
together in the data, and in a regular sequence. If we find the data conforming to
this pattern, we may conclude that there is a connection between them.
What is the probability, we may ask, of finding these three categories associated
in a regular sequence in the data? How often is jumping succeeded by clearing the
hurdle? How often is clearing the hurdle followed by landing? If these actions are
connected, then the probability is high that we shall observe them in the expected
sequence—unless other factors intervene. And here, of course, we must
acknowledge that it is not enough just to jump—our athlete must jump high
enough to clear the hurdle. We may find many—perhaps even a majority of—
examples in the data, where the jump is not high enough, the hurdle is not cleared,
and there is no happy landing on the other side. We must therefore introduce as a
condition or intervening variable, that the jump reaches a certain height. Then we
can check the data to see if our observations match our expectations.