“One cannot tell truth from falsity, one cannot tell an adequate answer to a problem from an irrelevant one, one cannot tell good ideas from trite ones, and one cannot evaluate ideas critically – unless they are presented with sufficient clarity.” Popper, “Reason or revolution?” in The Myth of the Framework (1994) pp70-1.

BAM201 Research Skills Session 8 Business and Management

Agenda A little more on data analysis Your work – individual tutorials Your work – get on with it… make use of the time and resources available this morning

Qualitative data We want our analysis to be rigorous! We want other people to be able to see clearly how we’ve analysed the data. Ideally, if another researcher were to follow your method of analysing the data they would reach the same conclusions.

Meaning versus Measurement

We may not be interested in the truth or accuracy of what our research participants say We are interested in how they perceive their reality

To grasp the meanings of research subjects, researchers ‘translate’ the subjects’ words into their own understandings Ghauri & Gronhaug (2005) p212 For example, via coding using themes

Coding Open coding – breaking data down and comparing and categorizing it Axial coding – putting data back together after open coding in new ways by making connections between categories

The idea of Grounded Theory Coding is tentative “The researcher’s interpretation of data shape his or her emergent codes” (Charmaz cited in Bryman & Bell (2003) p428) As more data is collected and examined the coding may be modified

The idea of Grounded Theory Sometimes it’s suggested that theory emerges from data Inductive vs deductive research (See Ghauri & Gronhaug (2005) pp ) But we should not think that theory- free observation or data collection is possible – it’s not!

An aside on induction and progress We do not learn by observation There is no inductive logic Deductive logic is useful only for criticism New theories require imagination We learn by trial and error

Other approaches to analysis looking for meaning Narrative analysis – interpreting the stories told by research participants Metaphor analysis

A reminder… If your data is boring then no amount of careful analysis will reveal anything interesting You must get your research subjects to talk openly about something interesting at the data collection stage!

I’d expect…. Introduction Data collection –Refer to literature you’ve used to guide your work, eg Gillham Data analysis –Refer to literature you’ve used to guide your work, eg Stewart et al Conclusions Reflections Writing up

Remember your reader!!! –Make the document fit for purpose What IS the purpose? Writing up

Robustness and clarity You will hopefully be drawing some conclusions –These are claims You hopefully want your claims to be true We can never be certain that our claims are true but if they are robust in the face of testing criticism then this is good

Robustness and clarity So be self-critical –Anticipate your reader’s criticisms and explain why these criticisms fail to damage your claims Don’t make claims that are easily shown to be false or highly dubious

Robustness and clarity Your task is to explain as clearly as you can why you think what you think about your research question

“The history of ideas… develops to an indispensable degree on the basis of criticism.” Bryan Magee, The Philosophy of Schopenhauer, p85