Chapter 13
QUALITATIVE ANALYSIS
Immersion in the Data
Just as you immerse yourself in the field when you are collecting data, in
qualitative analysis it is essential to immerse yourself in the data. This
happens in a number of ways. First, there is no substitute for doing your
own transcription. When you transcribe interview data from tapes, you
engage a number of senses and begin the immersion process. It is a good
idea to keep your field journal nearby to make analytic notes as you
transcribe. You will then read transcripts many times in order to become
completely familiar with the data. How many times? There is no set
number. However, a good guide is that if you come up with an analytic
insight, you know exactly where to go in any data to find supporting or
disconfirming evidence.
Preliminary Informal Analysis
Even as you are interviewing a participant or observing in the field, you
are already thinking analytically. Fortunately, as thinking human beings,
we can't help it. Again, be sure your field journal is handy, and write down
everything that comes to your mind. Do not trust your memory. No idea or
hunch is too insignificant to write down.
Analytic Memos
Analytic memos can be written anywhere: on a napkin, in your field
journal, in a transcript, even recorded at the end of an interview when you
are by yourself. Analytic memos are dated and include relevant
information, as well as the memo itself. Memos can be short or long, but
should contain enough information that you know what you were thinking
when you made them.
Finding Codes or Themes
A code is a concept that is given a name that most exactly describes what
is being said. Typically, in an interview transcript, the researcher might
highlight a word, phrase, sentence, or even paragraph that describes a
specific phenomenon. This word, phrase, sentence, or paragraph is a
meaning unit. After highlighting this segment of text, the researcher gives
it a name (code). The code should be as close to the language of the
participant as possible. The difference between a code and a theme is
relatively unimportant. Codes tend to be shorter, more succinct basic
analytic units, whereas themes may be expressed in longer phrases or
sentences.
Connecting Codes or Themes into Categories
After identifying and giving names to the basic meaning units, it is time to
put them in categories, or families. Similar codes all can be gathered
together into a category, or family of codes, and one might give them a
common code. Again, stay as close as you can to the language of
participants. However, as you gather codes into categories, and then
categories into larger more overarching categories, you will find that you
will necessarily have more abstract names for the categories in order to
make them more inclusive. A good guideline is, when you move to greater
abstraction, still use language that would be understandable to
participants. In grounded theory, the goal would be to ultimately have all
the data subsumed under one overarching core category. However, for
the most part having a very few top-level categories is fine. As you code
and categorize the data, also look for the interrelationships among the
various categories.
Searching for Confirming and Disconfirming Evidence
It is easy for researchers to find data to confirm our predisposing
assumptions and emerging hypotheses and to ignore data that contradict
them. As we analyze data, we read and reread both to confirm what we
are finding, but also to actively disconfirm. Periodically, it is helpful to take
each major section of your analysis, for example a working hypothesis,
and read through all the data to try to disprove it.
Building a Conceptual Framework or Theoretical Model
After extensive analysis, you will finally end up with an explanatory
framework that describes your results. It may be helpful to the reader to
see a figure with the interrelationships among the various parts of the
model. In some instances, researchers reconstruct a narrative to illustrate
their findings. Even as you build toward this framework, you will continue
to reanalyze your data. The emerging model should be examined and reexamined, compared and contrasted with all of the data and between the
various components of the model, and revised until no new changes need
to be made. Just as you collected data to the point of redundancy, you are
looking for a similar event in your analysis, called saturation. When all the
codes and categories are saturated, that means that (a) all the data are
accounted for, with no outlying codes or categories; and (b) every
category is sufficiently explained in depth by the data that support it.