An Introduction to Analysing Qualitative Data PAUL GREENBANK - PowerPoint PPT Presentation

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

Silverman (2013) ‘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).

Process of data analysis Organisation • Case data • Case records Immersion ‘Constant • Familiarisation comparative method’ Data • Core & sub- reduction, e.g. categories themes, codes

Identify Relationships Patterns Deviations Gaps

Numbers 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). 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)

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/

Matrix B1 B2 B3 B4 Gender F F M M Age 18 19 20 19 Qualifications Vocational Vocational + Vocational A-levels A-levels Social class Middle Middle Middle Working (ambiguity) (ambiguity) PDP Negative Positive Negative Positive (but boring!)

Cognitive map

Cognitive (mind) map using Inspiration iMindMap 7 http://thinkbuzan.com/prod ucts/imindmap/

Other issues  Role of intuition  Confirmation trap  Fuzzy generalisations

Writing-up