RMCP - all lectures for the second exam
Lecture 7: Qualitative Data Analysis.................................................................................... 2
Lecture 8: Qualitative data analysis II:.................................................................................9
Lecture 9: Validating questionnaires................................................................................. 15
Lecture 10: Focus group discussions............................................................................... 18
Lecture 11: Participatory research..................................................................................... 23
,Lecture 7: Qualitative Data Analysis
The analysis of qualitative data is a rigorous, logical process through which data is given
meaning. You are interpreting the data.
Assumptions about qualitative data analysis
- Reality is socially constructed
- Emic (insider’s point of view)
- Variables are complex, interwoven, and difficult to measure
- The researcher is his/her own instrument
- No standardised procedures
- Personal involvement and partiality
- Empathic understanding
Characteristics of a qualitative researcher
Reflexive awareness - ability to:
- Think abstractly
- Step back and critically analyse situations
- Recognize the tendency towards bias
Openness:
- Be flexible and open to helpful criticism
- Appreciative inquiry
Sensitivity:
- Sensitive to the words, interpretations and actions of respondents
When to think of analysis?
Starts already during the research design phase
During design: Devising frameworks, interview guides
- Constructing ways of looking, ways of understanding
During data collection:
- Questioning, probing, co-construction of meaning
Desk analysis afterwards:
- Coding the responses and discussions
- (Re-)construct relevant concepts and themes
- Organise around core generalisations or idea
Qualitative analysis principles
1. Noticing concepts
2. Collecting examples of these concepts
3. Analysing these concepts in order to find
commonalities
2
, Different types (degrees) of analysis
Content analysis: The purpose is to describe the characteristics of the
document’s content by examining who says what, to whom, and with what
effect and make inferences (may contain quantitative elements)
Thematic analysis: Thematic analysis as an independent qualitative
descriptive approach; mainly described as a method for
identifying,analysing,and reporting patterns (themes) within data
Grounded theory: The construction of theory through the open analysis of
data.
Codes: Word(s) or short phrase(s) that represents the essence orr key
attribute of narrative/verbal information.
- Used to categorize data
- Coding is the process of organising the data into ‘chunks’ (segments) that are alike
- Coded are developed into a ‘coding structure/guide’
Code structure/Code Guide:
- Compilation of emerging codes
- Brief definitions or properties for each code (can also include illustrative coded
pieces/quotes)
- Provides guidance for when and how to use the codes
- Will evolve throughout the analysis (refining)
- You continuously have discussions with your research team
Quotations:
- Brings reader to reality of the situation
- Support your analysis and findings
- Illustrative
- Range of issues
- Opposing views (between stakeholders)
- Do think of anonymity
Qualitative analysis steps:
1. Data curation → Transcribe (interview, field notes, etc)
2. Collect - code - collect - code etc. (familiarisation)
3. Read and reread, suspend initial interpretation. Focussed reading and open coding
4. Close examination, label text with keywords. Reviewing and axial coding.
5. Modify codes, remove duplications,
6. hierarchical order, integrate theory. Generate theory.
7. Look for connections that emerge from the data. Selective coding,
3
Lecture 7: Qualitative Data Analysis.................................................................................... 2
Lecture 8: Qualitative data analysis II:.................................................................................9
Lecture 9: Validating questionnaires................................................................................. 15
Lecture 10: Focus group discussions............................................................................... 18
Lecture 11: Participatory research..................................................................................... 23
,Lecture 7: Qualitative Data Analysis
The analysis of qualitative data is a rigorous, logical process through which data is given
meaning. You are interpreting the data.
Assumptions about qualitative data analysis
- Reality is socially constructed
- Emic (insider’s point of view)
- Variables are complex, interwoven, and difficult to measure
- The researcher is his/her own instrument
- No standardised procedures
- Personal involvement and partiality
- Empathic understanding
Characteristics of a qualitative researcher
Reflexive awareness - ability to:
- Think abstractly
- Step back and critically analyse situations
- Recognize the tendency towards bias
Openness:
- Be flexible and open to helpful criticism
- Appreciative inquiry
Sensitivity:
- Sensitive to the words, interpretations and actions of respondents
When to think of analysis?
Starts already during the research design phase
During design: Devising frameworks, interview guides
- Constructing ways of looking, ways of understanding
During data collection:
- Questioning, probing, co-construction of meaning
Desk analysis afterwards:
- Coding the responses and discussions
- (Re-)construct relevant concepts and themes
- Organise around core generalisations or idea
Qualitative analysis principles
1. Noticing concepts
2. Collecting examples of these concepts
3. Analysing these concepts in order to find
commonalities
2
, Different types (degrees) of analysis
Content analysis: The purpose is to describe the characteristics of the
document’s content by examining who says what, to whom, and with what
effect and make inferences (may contain quantitative elements)
Thematic analysis: Thematic analysis as an independent qualitative
descriptive approach; mainly described as a method for
identifying,analysing,and reporting patterns (themes) within data
Grounded theory: The construction of theory through the open analysis of
data.
Codes: Word(s) or short phrase(s) that represents the essence orr key
attribute of narrative/verbal information.
- Used to categorize data
- Coding is the process of organising the data into ‘chunks’ (segments) that are alike
- Coded are developed into a ‘coding structure/guide’
Code structure/Code Guide:
- Compilation of emerging codes
- Brief definitions or properties for each code (can also include illustrative coded
pieces/quotes)
- Provides guidance for when and how to use the codes
- Will evolve throughout the analysis (refining)
- You continuously have discussions with your research team
Quotations:
- Brings reader to reality of the situation
- Support your analysis and findings
- Illustrative
- Range of issues
- Opposing views (between stakeholders)
- Do think of anonymity
Qualitative analysis steps:
1. Data curation → Transcribe (interview, field notes, etc)
2. Collect - code - collect - code etc. (familiarisation)
3. Read and reread, suspend initial interpretation. Focussed reading and open coding
4. Close examination, label text with keywords. Reviewing and axial coding.
5. Modify codes, remove duplications,
6. hierarchical order, integrate theory. Generate theory.
7. Look for connections that emerge from the data. Selective coding,
3
