A Coggle Diagram about QA! Method
- Qualitative Methodology /
Different research approaches + methods
based on different philosophical assumptions
Data Analyses Techniques
- Quasi Statistical such as Content Analysis
Using word or phrase frequencies to determine the importance of terms and concepts
- Coding/Theme analysis - Demarcation of segments within data labelled with code (word/short phrase) linking back to research aims
- Recursive abstraction - Continual summarising of data (with reasoning at each step) data analysis technique that is very useful when analysing interview data.
Why is it useful:
The how of generating theory:
- When little is known about the topic area
- When there are no theories to explain the construct under investigation
- The researchers aim to develop new theories
- ULTIMATELY THE INTEREST IS NOT ONLY TO DESCRIBE but to develop theoretical explanations about why and how.
Process Steps (Odis Simmons)
- Data can be qualitative or quantitative
- Data collection and analysis is extremely iterative
- A version of thematic coding, but where the codes arises out of an interaction with the data.
- Theories are generated by the systematic comparative analysis of fieldwork data i.e moving back and forth between the identification of similarities and differences between emerging codes
- The theory can then be tested by further research.
- Theory verification should be possible by returning to the data.
- The process is inductive moves from the specific to the general unlike hypothesis testing which is deductive.
- Preparation: Minimise preconceptions. NO preliminary literature review. Establish a general research topic, but NO predetermined research “problem.”
- Data Collection: Interviews, combined with participant observation. But, any type of data can be used, including quantitative.
Initial analysis determines what to look and get for in the next data collection. Analysis and data collection continually inform one another.
3 Analysis: Constant Comparative Analysis i.e
Relating data to ideas, then ideas to other ideas.
- Substantive code - summarize empirical study
- Open Coding - Coding for anything and everything.
What is this data a study of?
What category does this incident indicate?
What is actually happening in the data?
- Selective Coding - Identifying the core variable and major dimensions have been discovered.
Closed coding - limiting the coding to those related to the core variable.
- Theoretical Coding - Showing howthe substantive codes may relate to each other as hypotheses to be integrated into the theory
), QA! Why Bother
To enquire about a particular area of study
- :space_invader: Need an initial, overall understanding of a problem space
- :cry: Need to understand a complex problem
- too many contextual problems
- sensitive issues
- :!?: Useful when needing to answer the why and how questions of a problem
, Data Analysis-
Analyse qualitative data and reduce it down to quantitative data
OR.. qualitative data could be used to provide context to quantitative data
OR.. qualitative data could be treated as meaningful in its own right
and QA! Definitions
A code is a label which exemplifies an idea and is applied to chunks of data
In the initial stage it is used to reduce the data and later on in the analysis stage as subtle process to develop labels that describe data which pinpoint examples of relevant phenomena to then find commonalities, differences patterns and structures