Basic guideline for qualitative data analysis
- Know yourself, your biases, and preconceptions.
- Know your question/s.
- Seek creative abundance. Consult others and keep looking for alternative interpretations.
- Be flexible.
- Exhaust the data.
- Celebrate anomalies.
- Get critical feedback. The solo analyst is a great danger to self and others.
- Be explicit. Share the details with yourself, your team members, and your audiences.
Preliminary task in analysis
- Arrange for secure storage of original materials.
- Transcribe interviews or otherwise transform raw data into usable formats.
- Make master copies and working copies of all materials.
- Arrange secure passwords or other protection for all electronic data and copies.
- When ready to begin, read all the transcripts repeatedly—at least three times—for a sense of the whole. Don’t force it—allow the participants’ words to speak to you.
State 6 data analysis method
- Content analysis
- Narrative analysis
- Discourse analysis
- Grounded theory (GT)
- Interpretive phenomenological analysis (IPA)
- Thematic analysis
Describe content analysis
Used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication.
Describe narrative analysis
Listening to people telling stories and analysing what that means.
Describe discourse analysis
Analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place
in.
Describe grounded theory analysis
To create a new theory (or theories) using the data at hand, through a series of “tests” and “revisions.”
Describe interpretive Phenomenological Analysis
Help to understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation.
Describe thematic analysis
Looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. It takes bodies of data and groups them according to similarities – in other words, themes. These themes help us make sense of the content and derive meaning from it.
Types of thematic analysis
- Inductive thematic analysis (data are
interpreted inductively, that is, without bringing in any preselected theoretical categories) - Theoretical thematic analysis (participants’ words are interpreted according to categories or constructs from the existing literature)
Steps in conducting data analysis
- Familiarizing yourself with the data (listen, check, read the transcript)
- Generating initial codes
- Searching for themes
- Reviewing themes
- Defining and naming themes
- Producing the report
