Qualitative research - data analysis Flashcards

1
Q

What is the purpose of qualitative data analysis?

A
  • 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
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2
Q

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

A
  • 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
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3
Q

How are qualitative data analysed?

A
  • quantification (counting - e.g. count no. of times something is said or individuals contribute)
  • Thematic content analysis
  • Framework analysis
  • Transparent analysis
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4
Q

Should qualitative researchers quantify?

A
  • 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…
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5
Q

Challenges for qualitative researchers?

A
  • 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
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6
Q

Transparent analysis?

A

data analysis does need to be reasonably transparent

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7
Q

What are the stages of data analysis?

A
  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
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8
Q

What is thematic content analysis?

A
  • 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
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9
Q

What is framework analysis?

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

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

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10
Q

What happens when data is interrogated?

A
  • do themes apply in certain sub-groups?
    e. g. males, mature students
  • analysis of negative cases
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11
Q

How do researchers check/validate their analyses?

A
  • 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)
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12
Q

What are the final stages of data analysis?

A
  • 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
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13
Q

How is the data presented?

A
  • 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
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