Qualitative research - data analysis

Question 1

What is the purpose of qualitative data analysis?

Answer 1

• long transcriptions and recorded field notes - data needs to be reduced and have order imposed
  1. DESCRIBE
  2. DEVELOP THEORY
  3. DEVELOP HYPOTHESES - for other research

Question 2

What is meant by ‘all data analysis is data reduction’?

Answer 2

• schemes for reducing data - developed during and after data collection
• during = concurrent data analysis
• GT = constant comparative analysis to develop hypotheses + test them out at subsequent interviews

Question 3

How are qualitative data analysed?

Answer 3

• quantification (counting - e.g. count no. of times something is said or individuals contribute)
• Thematic content analysis
• Framework analysis
• Transparent analysis

Question 4

Should qualitative researchers quantify?

Answer 4

• some would say no
• BUT useful for esuring researcher focuses on what is really in the data
• can encourage rigour and honesty
• can be useful to do some quasi (counting) when reporting findings using language such as ‘all’ ‘some’ of the PPs said…

Question 5

Challenges for qualitative researchers?

Answer 5

• no rules for data analysis
• no statistical packages to do data analysis for you
• makes qual. data analysis difficult to describe
• have to make sense of loads of data
• interviews often 90 mins
• labour intensive
• presentation = tricky
• if yyou reduce qual. data too much = sense lost and meaningless

Question 6

Transparent analysis?

Answer 6

data analysis does need to be reasonably transparent

Question 7

What are the stages of data analysis?

Answer 7

1. tidy up data
2. check transcription against recording
3. develop method to index the materials so they are easily accessible
4. commonly enter the data into a qualitative data analysis programme e.g. NVivo, Atlas ti - help to organise data and clarify thinking

Question 8

What is thematic content analysis?

Answer 8

• go through transcript looking for themes (things that crop up over and over)
• ‘commonalities’ emerge from data
• themes = given a code (word/phrase)
• codes then collapsed in to categories
• constantly reducing data –> more manageable/meaningful
• looking for variation within data

Question 9

What is framework analysis?

Answer 9

• take a framework to the data and put it into categories

- framework can come from pre-existing theory or initial thematic analysis

Question 10

What happens when data is interrogated?

Answer 10

• do themes apply in certain sub-groups?
  e. g. males, mature students
• analysis of negative cases

Question 11

How do researchers check/validate their analyses?

Answer 11

• if more than one researcher - all analyse and then compare
• can also present analyses back to PPs
• member checking - but analysis is interpretive (no right or wrong)

Question 12

What are the final stages of data analysis?

Answer 12

• interelate the themes - so creates an integrated whole/ tells a story
• difficult process - qual. data analysis packages can help e.g. diagramming - but ultimately relies on creativity and intellectual rigour of researcher

Question 13

How is the data presented?

Answer 13

• in a paper - logical journey through the data is presented under a no. of themes
• typically as quotes followed by some analysis
• easier to present qual. findings in a book
• lengthy quotes, followed by clear analysis and interpretation = good way