1. Dissertation module Qualitative Data Analysis
15 March 2017

2. Learning Outcomes By the end of this session, you will be able to
be familiar with qualitative data
understand the basics of qualitative analysis
understand the challenges of analysing qualitative data

3. The Purpose of Qualitative Research
To produce findings
To generate knowledge
To answer research questions
The data collection process is not an end in itself
The culminating activities of qualitative research are analysis, interpretation, and presentation of findings.
To make sense of what people have said
To identify emerging patters
To understand meaning

4. What is Qualitative Data?
Qualitative data are nonnumeric forms of information. Examples
Interview and focus group transcripts
Researchers’ field notes
Video
Audio recordings
Images
Documents (reports, meeting minutes, websites, blogs, s)

5. The Challenge of Qualitative Data Analysis
Practical:
To make sense of massive amounts of data,
To manage and reduce the volume of information,
To identify important patterns
To develop a framework for understanding and communicating what the data reveal
Theoretical and methodological
No shared rules for drawing conclusions and verifying validity of these conclusions
Questions of rigour and credibility

6. Strengths/ Weakness of Qualitative Data
‘..the human element of qualitative inquiry is both a strength and weakness –
its strength is fully using human insight and experience,
its weakness is being so heavily dependent on the researcher’s skill, training, intellect, discipline, and creativity.
The researcher is the instrument of qualitative inquiry, so the quality of the research depends heavily on the qualities of that human being’ (Patton, 1988)

7. Initial Data Analysis
Engaging with the data is an ongoing process; it is not sequential.
Writing field-notes immediately after interview
Updating research journal
Transcribing interviews
Reviewing interview
Discussion with colleagues and peers
Starting data analysis as soon as you collect data

8. Noticing, Collecting and Thinking about interesting things (Seidel 1998)
Interlinked and cyclical process of reflecting on the data. Thinking about things, noticing further things and collecting them.
Noticing interesting things in the data relating them to other key themes and assigning ‘codes’ to them.

9. Content Analysis
A research tool used to determine the presence of certain words or concepts within texts
A quantitative method used to analyse qualitative work.
Allows for a better understanding of how topics are being discussed in such things as the news media
Quantifying and understanding the presence, meanings and relationships of such words and concepts

10. Quantitative analysis of qualitative data
Not polar opposites. Quantitative approaches can be used to help understand qualitative data
Apply quantitative labels to informants based on demographics
Attributes and other things that can be counted or can be answered yes/ no
Age
Gender
Ethnicity
Occupation
Length of time in current job
Highest educational attainment
Caring responsibilities
Technical details
How informants were recruited
Interviewer name

11. Quantitative analysis to illuminate qualitative data
Demographic data and other attributes can help identify and understand patterns in qualitative data
E.g. what did the white British women say about a particular code e.g. return to work after motherhood
Problems
These labels can be ambiguous.
Who is included in the category White?
Using them may take for granted the social forces that came together to construct them. One purpose of qualitative research is to understand how social forces created these categories.

12. Thematic Analysis
Involves identifying, analysing and reporting patterns (themes) within the data. The body of text is organised into specific themes so that the content can be summarized.
Phase 1: Become familiar with your data
Actively read all your transcripts at least once & make notes.
Phase 2: Create initial codes
A code is the most basic building block of the raw data & identifies the data that is of most interest to the researcher.

13. Thematic Analysis
Phase 3: Searching for themes Grouping the codes into potential themes, examining whether the themes relate to each other. Phase 4: Reviewing themes Does each code have enough data & are the data similar? Phase 5: Defining & naming themes Phase 6: Producing the report

14. What is a theme
Thematic analysis is still relatively underdeveloped – it’s not clear what a theme is. However...
A category identified by the analyst through his/her data
Relates to his/her research focus/question
Builds on codes identified in the transcripts
(Bryman, 2012)
Code: Pushing Code: Name-calling
Code: Fighting Code: Threatening
Code: Scratching Code: Laughing at

15. Categorising aka Coding
Bringing meaning to the texts
Identify key themes and patterns
Key ideas, concepts, descriptions and terminology
Organise these themes into coherent categories
Using pre-set themes related to research questions or interview questions
Using emergent themes – key themes that emerge from the data (very unusual not to do this)
Assign codes (words, phrases, symbols) to label and describe these ideas and themes

16. Identifying patterns within categories (codes)
Bring together all of what has been said about the issue
What similarities and differences in what people say
Contextualise the data collected under the codes.
In what circumstances do informants say things
Summarise these points
How often do informants talk about the issue
What does that say about its importance
What issues have not emerged and why
e.g. in SAG informants rarely discussed racism except in relation to merit awards

17. Identifying patterns between the categories (codes)
Do people tend to talk about two or more codes at the same time?
What relationships does it suggest between these two issues?
E.g. racism and merit awards
Does this suggest a perception of cause and effect?
What data is there to back this up?
Is there any data that contradicts this?
Can you contextualise it?
Narrative data generally will not support cause and effect but it highlights perceptions and understandings.

18. Tools and approaches to QDA
Manual or computerised
Manual
Print offs of transcripts
Code with highlighter pen and or scissors
Store coded data in boxes, files, envelopes
Manual searches
Problematic for large datasets and projects.
Computerised QDA
large data sets
General data management software Word, Excel, Access
Specialised QDA Nvivo

19. Interpretation of data
What does this analysis actually mean?
How does it address the research questions or hypotheses?
What is significant in this analysis?
I.e. what could it contribute to knowledge, policy, informants or professional practice
List the key findings and apply them to the research questions and illustrate their significance.
Are there any good quotes to back up your argument
Is there anything in the data that contradicts what you are arguing?
Develop a template for the research report using these key findings and supporting evidence.

20. From codes to themes to theory (adapted from Saldana, 2009)

21. An example
Research question: What are the housing histories of students on the RCC module? One objective: to explore the range of students’ experiences of housing, where they have lived from childhood to now.

22. Analysis of in-depth interviews
Content analysis – extracting information that relates to research objectives or questions
Themes – organising/categorising information according to research questions
Use of quotes
Presentation in a report

23. References
Bryman A., (2012) Social Research Methods. 4th ed, UK: Open University Press (chapter 13, 24)
Patton, M.Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.
Saldana, J. (2009). The coding manual for qualitative researchers. Los Angeles, CA: SAGE
Seidel, (1998), Qualitative Data Analysis. In The Ethnograph v5 user's manual, Appendix E. Denver, CO: Qualis Research Associates.   March 2005). Google Scholar
Whittaker A (2012) Research skills for Social Work, Exeter: SAGE Publications Ltd (chapter 7)