Handling Qualitative Data Flashcards
What is a transcript?
- A necessary step on the way to interpreting textual qualitative data
- No ‘standard’ established
What are two forms of transcripts?
Precise transcription
-Verbatim transcription
What can be used in precise transcriptions?
-Linguistic analysis dictates maximum exactness (no para-phrasing) (Jefferson)
. Symbols used in conversation analysis
Explain verbatim transcription
-As much as is required by the research question
. Line numbers down the side allowing quick reference to quotes
What are some key points about transcription?
. The level of transcription has an influence on the quality of data for interpretation
. Transcription takes time
. Badly recorded interviews take longer to transcribe
. The words of the interviewer should also be included
. Transcription services – quality check
Explain the interpretation stage of data analysis?
• Locate meaning in the data
• Skilful expedition-usually better if done by researcher
• Coding – a label, note, a query, or a tag
associated with an extract of data
• Theme – a group of coded data which
represent a similar pattern / concept
Thematic Analysis (next Semester)
Explain coding
- Starting blocks for interpretation of data
- Used to categorise the text and develop theory
- Codes are applied to a ‘unit’ of textual data
- A code is a ‘label’ that captures a segment of the data
What are the two types of coding?
•Deductive (Top down / A priori). Driven by a specific
research question
• Inductive (Bottom up / Emergent). Codes are linked to
the data
What should be coded?
• Themes, topics, concepts, terms, phrases, key
words
What should you look for when coding?
- What is happening?
- What are the people saying?
- What emotions / attitudes might they be implying
When is deductive coding used?
Used when a predetermined theory / concept needs evidencing
When is inductive coding used?
Used to build new theory or develop new concepts.
How can the analysis be developed?
- Codes need to be organised-e.g. Similar concepts
- Categorise them into separate groupings (sub-themes) and organise based on similarities
- Refine into general themes (patterns in the data)
- Reliant on researcher interpretive skill – keep analytical notes /memos
- Process of reducing the data so it is manageable and understandable