essential-guide-to-qualitative-in-organizational-research

–––––––––––––––––––––––––––––––––––––––––––––––––––––– ANALYTIC INDUCTION–––––––––– 177First, a criticism of AI argues that due to the small samples used, the method can rarelymake claims about the representativeness of its samples and therefore any attempt atgeneralizing is tenuous. For Mitchell (1983) such a criticism shows a confusion between theprocedures appropriate to making inferences from survey research and those which areappropriate to case study work. He argues that analytical thinking about survey data is basedupon both statistical and logical (namely causal) inference and how there is a tendency to elidethe former with the latter in that ‘the postulated causal connection among features in a samplemay be assumed to exist in some parent population simply because the features may beinferred to co-exist in that population’ (Mitchell, 1983: 200). In contrast inference in casestudy research can only be logical and derives its external validity not from itsrepresentativeness but because ‘our analysis is unassailable’ (Mitchell, 1983: 200). Suchanalytical thoroughness is achieved in AI by eliminating exceptions and revising hypothesesso that statistical tests are actually unnecessary once negative cases are removed (Fielding andFielding, 1986: 89). Thus in AI extrapolation is derived from logical inference based upon thedemonstrated power of the inductively generated and tested theoretical model ‘rather than therepresentativeness of the events’ (Mitchell, 1983: 190).Secondly AI’s procedures entail the movement from the ‘thick’ description andcategorization of actors’ phenomenological worlds to propounding theoretical explanationsof those categories. This entails an initial (re)presentation of actors’ internal logics groundedin verstehen in order to formulate categories. However, in order to avoid analytic interruptusAI requires the researcher to shift to a form of analysis that entails his/her imposition of anexternal logic which exists independently of, and explains, the actors’ internal logics. Clearlythis shift entails an overt form of what Burrell and Morgan term ‘ontological oscillation’(1979: 266) – the initial adoption of a subjectivist stance with the subsequent introduction ofincommensurable objectivist assumptions. Now the question for AI is whether, as Burrell andMorgan claim (1979), such oscillation poses a contradiction which should be avoided, or asWeick (1995: 34–8) argues, such oscillation is a vital element in sensemaking that helps usunderstand the everyday actions of people. Basically the latter view would support AI as it ispresented here, whereas the former would either necessitate the discarding of aphenomenological starting point for AI, or AI’s dissolution through a limitation tophenomenological ‘thick description’.Finally there remains a basic question regarding the extent to which ‘pure’ induction ispossible. Ironically, the claim that it is possible shares with logical positivism the implicitassumption that there exists a theory neutral observational language in which the researcheris construed as neutral conduit of cultural experience who can objectively elucidate andpresent the ‘facts’ of a cognitively accessible empirical world. For Hammersley (1992) suchissues are especially problematic for any approach which is committed to accessing members’phenomenological worlds so as to reveal their subjectivities. It creates a contradiction betweenan objectivist impulse that emphasizes how phenomenological accounts should correspondwith members’ subjectivity and a phenomenological impulse that suggests that people sociallyconstruct versions of reality – culturally derived epistemic processes to which researchers arenot immune. Perhaps a key task for any methodologist with a subjectivist agenda is to resolvethe problems posed by these epistemological ambiguities while avoiding the spectre, andquagmire, of relativism (see Alvesson and Deetz, 2000).