Non-standardised and complex in nature Demanding process- not an ‘easy option’ This happens during data collection, therefore preparing is key, recordings, transcripts etc Understanding characteristics and language You do this by finding patterns in your data and by producing explanations Reflection

1. Qualitative
Analysis
Dr Ryan Thomas
Williams

2. Data analysis
• Non-standardised and complex in nature
• Demanding process- not an ‘easy option’
• This happens during data collection, therefore preparing is key, recordings, transcripts etc
• Understanding characteristics and language
• You do this by finding patterns in your data and by producing explanations
• Reflection

3. Data analysis
• Qualitative data analysis involves some form of data transformation
• Collect the data
• Prepare the data (sorting, retrieving, indexing and handing of the qualitative data)
• Process the data through analytic procedures
• The data is processed so that it is clear, understandable, insightful, trustworthy and even
original analysis
• Your data processing might involve identifying the subject matter as described by your
participants or your analysis may involve interpretation and retelling, involving your
imagination and speculation (Riessman, 1993 & Dexin, 1997)

4. Transcribing data
• Prepare the data- word/text files, visual data files, audio files
• Must make a decision on transcribing the data
• Full transcripts- word for word (verbatim) is accurate and detailed yet is time-consuming
(1:6 - 1:9) and omits nonverbal aspects.
• Partial transcripts select important parts of the data directly from the original source.
Can include verbatim and short extracts. Ensures that the bigger picture is not lost. A
disadvantage is that this is data interpretation and can reflect the researcher as much as
the data.

5. Discourse analysis
• Does not use coding as they keep together the text rather than fragmenting it via codes
• Argue that any text can have several discourses, and a single text can have several
meanings
• Explores linguistic features that links the language to society
Example: A teacher threatening a student with detention if they don’t stop speaking in class
can be classified as a repressive power. Normalized power, in contrast, isn’t actively
asserted. It’s the power that makes students not want to talk in class. It’s manifested in the
subtle clues from our environment that tell students how to behave.

6. Narrative analysis
• Story telling with an individual perspective
• Should not take data at face value as they acknowledge this is simply a narrative
summary- interpretivist (author’s construction)
• Researcher conducting an in-depth interview, or a series of in-depth interviews with an
individual over an extended period of time
Example: officials tend to feel superior and powerful whilst inmates tend to feel inferior and
powerless (Goffman 1968)

7. Grounded theory
• Create a theory that is grounded in data
• Data is coded as to they way that it emerges with the text
• The original, emergent model- does not start with a literature review as it could lead to
preconceived ideas (open coding)
• The revised, systematic model- greater emphasis on the literature review, more
prescriptive (axial coding)
• The constructivist model- argues that concepts are not ‘discovered’ but constructed
• Collected data is broken down into component parts and given names.
• Researchers’ interpretation of the data shape the codes

8. Grounded theory coding

9. Thematic analysis
• Part of coding and content analysis
• A strategy for combing other approaches (Braun & Clarke 2006). Searching across a data
set to find repeated patterns of meaning…..
• A method of identifying, analysing and reporting patterns
• Start point- notice patterns
• End point- report meaning of patterns
• Takes place throughout data collection

10. Thematic
Analysis

11. Thematic Analysis Example

12. Coding – What to look for
1. Behaviours, specific acts – seeking reassurance, showing off.
2. Events – short once in a life time.
3. Activities – longer duration involving other people in settings.
4. Practices or tactics.
5. General conditions experienced by people.
6. Meanings, what symbols and words are participants using to describe a phenomena.
7. Participation in new activities/settings.
8. Relationships or interaction.
9. Consequences.
10. Settings.
11. Conditions or constraints.
12. Reflexive.

13. Coding
• Collected data is broken down into component parts and given names.
• Researchers interpretation of the data shape the codes.
• Theoretical saturation:
• Coding of data is reached and no further review needed.
• Collection of more data to illuminate the concept is no longer needed.
• Constant comparison – between the codes and indicators.

16. Stages of Coding
• In coding – data is treated as indicators of the concepts –much the same as when you devise your
questions/measures.
• Open coding – categories or themes are developed, break down, compare, categorize,
conceptualise.
• Axial Coding – categories and themes are interconnected, put back together in new ways.
• Selective coding – connects categories and builds to a theoretical proposition. Select one core
category that becomes the story line and link relationships to the other categories.
(Glaser & Strauss 1967)