1. Qualitative Data Analysis
Chapter Eight
Qualitative Data Analysis

2. Beginning Analysis during Data Collection
Data collection and analysis should be a simultaneous process in qualitative research.
Analysis becomes more intensive as the study progresses and once all the data are in.
By the time you are ready to analyze and write up your findings, you should have a set of tentative categories or themes and be organizing and refining rather than beginning data analysis.

3. Suggestions for Analysis during Data Collection
Make decisions about the study’s scope and type.
Develop analytic questions.
Use emergent results to plan data collection.
Write “observer’s comments” and memos.
Try out ideas and themes on participants.
Begin exploring the literature while in the field.
Play with metaphors, analogies, and concepts.
Use visual devices.

4. Managing Your Data Inventory, organize, and label all your data.
Devise an organizing scheme early in your study.
Track your insights while preparing your data for analysis, including while transcribing interviews.
Keep a backup of your entire data set, along with your organizing scheme.
Data management can be manual and/or electronic.

5. How to Analyze Qualitative Data
Levels of Qualitative Data Analysis:
Concrete description of observable data
Using concepts to describe phenomena
Making inferences, developing models, or generating theory.
Process:
Break data down into bits of information.
Assign these bits to categories or classes
Refine the criteria for categorizing data.
Revise the categories.

6. Category Construction
Think about the purpose of your study
Look at the data through the lens of the study’s epistemological framework.
Code your data (open coding).
Identify the main themes/insights/“answers” to your research questions.
Test: Do the individual data bits support those themes/insights/answers?
Combine the codes into fewer, more comprehensive categories.

7. Working with Categories
Sort all your scheme’s evidence into categories.
There should be a minimum of “uncategorizable” but useful/relevant data.
Each unit of data should include original identifying codes so you can locate its source.
Name categories consistently with the study’s orientation.
The number of categories should be manageable.

8. Computers and Qualitative Data Analysis
You can use specialized software (CAQDAS = Computer Assisted Qualitative Data Analysis) or adapt basic productivity software for these data management tasks:
Data preparation--creating a clean record
Data identification—coding data segments
Data manipulation--searching, sorting, rearranging
Advantages: Saves time; closer examination of data; enables visual modeling; allows import of resources.
Limitations: Cost, overkill, potential for distancing you from your data, learning curve.

9. Data Analysis and Types of Qualitative Research
Phenomenological Analysis: Analytical techniques address the researcher’s experience of a phenomenon, as well as that of participants. Grounded Theory: In addition to open and axial coding, the researcher performs selective coding in order to develop a core category, propositions, and hypotheses. Ethnographic Analysis: The researcher may employ a preexisting category scheme or derive one from the data, using an insider or outsider perspective. The medium affects analyses of online cultures.

10. Data Analysis and Types of Qualitative Research, cont’d.
Narrative Inquiry: Analysis may be either holistic or categorical, and may incorporate the techniques of a particular discipline or ideological perspective.
Case Studies: Data is voluminous and may include disparate or apparently contradictory information, so good data management is critical.
Action Research Studies: Analysis focuses not only on what happens, but also on how it happens over the course of the action research cycle of plan, act, observe, reflect.