Lecture 10: From data analysis to reporting Flashcards
Reduction of data depends on three things
- What data do you have
- What is research perspective that you take
- What are your objectives
How to do data reduction
- labelling
- causal analysis
- matrix
Content analysis
Making interferences about data by systematically identifying special characteristics within them
Characteristics, content analysis
- common classes=everyday categoreis
- special classes=specialist categories
- theoretical classes=arise in process of analysis
Secondary analysis
Integration of core categories into theory
steps:
- finding a storyline around core categoreis
- validate relationships between categories against data
- re-iterate
Moments of analysis
- Devising frameworks, interview guide
- Interview themselves
- Desk analysis afterwards
Member check
It is a preliminary analysis of interview
Integrate interpretation
Done after coding and done some analysis
2 starting points for data analysis
- procedures
x coding
x indexing
x categorising - creativity
x interpreting
x exploring relations
What is data analysis and two ways to do it
Actively working with data
- reduction
- complication: by looking for patterns at higher levels of abstraction