An Introduction to

Analysing Qualitative

Data PAUL GREENBANK

CENTRE FOR LEARNING AND TEACHING

Before we begin …

Qualitative data comes in many different forms

It is worth emphasising that effective analysis is

pointless if we do not have high quality data

‘No matter how elegant your original research

proposal its application to your first batch of

data is always salutary. In most qualitative

research sticking with your original research

design can be a sign of inadequate data

analysis rather than demonstrating a welcome

consistency’ (p. 234).

Silverman (2013)

Process of data analysis

Organisation• Case data

• Case records

Immersion

‘Constant comparative

method’

• Familiarisation

Data reduction, e.g. themes, codes

• Core & sub-categories

Identify

Relationships

Patterns

Deviations

Gaps

Numbers

Gherardi and Turner (1999) accept there is resistance to

the use of numbers in qualitative research but argue that,

‘If we are to understand the natural or social world ‘with no

holds barred’ then we need to deploy whatever

appropriate means come to hand’ (p. 107)

Silverman (1993) states, ‘If you are trying to get some

feel about the data as a whole … it may sometimes be

useful to use certain quantitative methods, however

crude they may be’ (p. 204).

Computers

Computer assisted qualitative data analysis software

(CAQDAS)

Thomas (2013, p. 244):

‘Nothing of course, substitutes for your intelligent reading of the data, and

this to my mind is the main danger of software in qualitative data analysis: it

leads you to a false belief that something else is going to do the hard work

for you. My one trial of CAQDAS left me disappointed and I have never

used it again. It left me believing that there’s no substitute for a good set of

highlighters from W.H. Smith, a pen and paper, and a brain’.

Alternative view:http://www.youtube.com/watch?v=hkcg0IJFy1M

CAQDAS Networking Project: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing/

B1 B2 B3 B4

Gender F F M M

Age 18 19 20 19

Qualifications Vocational Vocational +

A-levels

Vocational A-levels

Social class Middle

(ambiguity)

Middle Middle

(ambiguity)

Working

PDP Negative Positive Negative Positive (but

boring!)

Matrix

Cognitive map

Cognitive (mind) map using Inspiration

iMindMap 7

http://thinkbuzan.com/prod

ucts/imindmap/

Role of intuition

Confirmation trap

Fuzzy generalisations

Other issues

Writing-up