Chapter 13QUALITATIVE ANALYSISQUALITATIVE ANALYSISImmersion in the DataJust as you immerse yourself in the field when you are collecting data, inqualitative analysis it is essential to immerse yourself in the data. Thishappens in a number of ways. First, there is no substitute for doing yourown transcription. When you transcribe interview data from tapes, youengage a number of senses and begin the immersion process. It is a goodidea to keep your field journal nearby to make analytic notes as youtranscribe. You will then read transcripts many times in order to becomecompletely familiar with the data. How many times? There is no setnumber. However, a good guide is that if you come up with an analyticinsight, you know exactly where to go in any data to find supporting ordisconfirming evidence.Preliminary Informal AnalysisEven as you are interviewing a participant or observing in the field, youare already thinking analytically. Fortunately, as thinking human beings,we can't help it. Again, be sure your field journal is handy, and write downeverything that comes to your mind. Do not trust your memory. No idea orhunch is too insignificant to write down. Analytic Memos Analytic memos can be written anywhere: on a napkin, in your fieldjournal, in a transcript, even recorded at the end of an interview when youare by yourself. Analytic memos are dated and include relevantinformation, as well as the memo itself. Memos can be short or long, butshould contain enough information that you know what you were thinkingwhen you made them.Finding Codes or ThemesA code is a concept that is given a name that most exactly describes whatis being said. Typically, in an interview transcript, the researcher mighthighlight a word, phrase, sentence, or even paragraph that describes aspecific phenomenon. This word, phrase, sentence, or paragraph is ameaning unit. After highlighting this segment of text, the researcher givesit a name (code). The code should be as close to the language of theparticipant as possible. The difference between a code and a theme isrelatively unimportant. Codes tend to be shorter, more succinct basicanalytic units, whereas themes may be expressed in longer phrases orsentences.Connecting Codes or Themes into CategoriesAfter identifying and giving names to the basic meaning units, it is time toput them in categories, or families. Similar codes all can be gatheredtogether into a category, or family of codes, and one might give them acommon code. Again, stay as close as you can to the language ofparticipants. However, as you gather codes into categories, and thencategories into larger more overarching categories, you will find that youwill necessarily have more abstract names for the categories in order tomake them more inclusive. A good guideline is, when you move to greaterabstraction, still use language that would be understandable toparticipants. In grounded theory, the goal would be to ultimately have allthe data subsumed under one overarching core category. However, for themost part having a very few top-level categories is fine. As you code andcategorize the data, also look for the interrelationships among the variouscategories. Searching for Confirming and Disconfirming EvidenceIt is easy for researchers to find data to confirm our predisposingassumptions and emerging hypotheses and to ignore data that contradictthem. As we analyze data, we read and reread both to confirm what weare finding, but also to actively disconfirm. Periodically, it is helpful to takeeach 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 ModelAfter extensive analysis, you will finally end up with an explanatoryframework that describes your results. It may be helpful to the reader tosee a figure with the interrelationships among the various parts of themodel. In some instances, researchers reconstruct a narrative to illustratetheir findings. Even as you build toward this framework, you will continueto reanalyze your data. The emerging model should be examined and re-examined, compared and contrasted with all of the data and between thevarious components of the model, and revised until no new changes needto be made. Just as you collected data to the point of redundancy, you arelooking for a similar event in your analysis, called saturation. When all thecodes and categories are saturated, that means that (a) all the data areaccounted for, with no outlying codes or categories; and (b) everycategory is sufficiently explained in depth by the data that support