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DISMANTLING THE QUANTITATIVE – QUALITATIVE DIVIDE Comments On Hypothesis Testing, Induction, Statistics, Fiction And Epistemological Anarchy. Presentation at the 3rd International Conference on Interdisciplinary Social Sciences, Prato, Italy, July 2008

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  1. DISMANTLING THE QUANTITATIVE – QUALITATIVE DIVIDEComments On Hypothesis Testing, Induction, Statistics, Fiction And Epistemological Anarchy Presentation at the 3rd International Conference on Interdisciplinary Social Sciences, Prato, Italy, July 2008 Michael Wood (michael.wood@port.ac.uk) and Christine Welch Portsmouth University Business School, UK There is a revised draft paper at http://userweb.port.ac.uk/~woodm/QualQuant.pdf and this presentation is at http://userweb.port.ac.uk/~woodm/QualQuant.ppt

  2. My background …

  3. We suspect this presentation may be a bit of a mess because it’s largely about things which don’t make too much sense. So we’ll try and impose a clear framework …

  4. What conceptual framework is best for understanding research? • Perspective 1 or • Perspective 2 or • Perspective 3 or • Perspective 4 Which do you think will be the winner?

  5. Positivism Generalisation through statistics Research progresses through hypotheses and deductions Observer independent Large random samples Etc, etc Social Constructionism Generalisation through theoretical abstraction Research progresses through rich data and induction Observer part of study Small purposive samples Etc, etc Perspective 1: Two kinds of research Based loosely on Easterby-Smith et al (2002: 30). They list 8 dimensions. Other authors may use different labels for the two types: commonest probably Quantitative vs Qualitative.

  6. The two kinds of research? • Quantitative / positivist / deductive • Hard and spiky • Qualitative / phenomenological / social constructivist / interpretivist / etc • Soft and cuddly Often suggested that researchers need to choose one or the other.

  7. Which side are you on? • To hard and spiky people, soft and cuddly research is lacking in rigour • To soft and cuddly people, hard and spiky research is superficial and lacking in richness and relevance … but is this a genuine dichotomy?

  8. No …

  9. Perspective 2: More than two types of research: e.g. …. Easterby-Smith et al (2002) list 8 bipolar dimensions: this leads to the possibility of 28 or 64 types of research.

  10. What does qualitative mean? • General term from right hand side of Perspective 1 ………? • Opposite of statistical ……… ? • Focussed on qualities not quantities … ? • Detailed information / in depth analysis (eg not superficial questionnaire) …… OK

  11. But … • Why not use different approaches in one project? • E.g. Glebbeek and Bax (2004) found statistical evidence for the hypothesised inverted U shaped relationship between staff turnover and firm performance, but why not back this up with case studies to look at possible reasons for this effect? • Britten et al (2000) identified categories of misunderstanding between doctors and patients, but why not do a statistical survey to see how common each category is? • So …

  12. Perspective 3: Multimethodology or Mixed methods Use different approaches in one project …

  13. But … • There are problems with many of the bipolar dimensions used to pigeonhole research • Concepts used may be vague, ambiguous or prone to misinterpretation • May not be bipolar: reasonable approaches may be omitted

  14. Two bipolar dimensions? “Qualitative” Statistical Hypothetico-deductive “Inductive” “ …” indicates vague terms we don’t like

  15. Or … do these fit on the same dimension? Deterministic laws Empirical possibilities X “Qualitative” Statistical ? ? Fictional possibilities Is this one dimension?

  16. Extended statistical – non-statistical dimension • Deterministic laws – what always happens • Statistics – what sometimes happens • Illustrative inference – what has happened at least once (some “qualitative research”) • Fiction – what is possible / imaginable

  17. And what about this … ? Deductive (e.g. applying a model) X Hypothetico-deductive Inductive Using a framework or paradigm to define questions Much research is neither hypothetico-deductive nor inductive. There is no obvious linear dimension here, which is why we’ve made the layout of this slide a bit of a mess. So …

  18. Fourth perspective • Avoid unhelpful concepts. If in doubt, shut up, or use more straightforward language! • Induction? • Qualitative? • etc • No useful general categorisation schemes for research (=only useful grand narrative) • If you stick to such a scheme you risk ignoring useful possibilities • Epistemological anarchy. (= Postmodernism?) • Feyerabend: “anything goes” • But some concepts are worth careful thought …

  19. Statistical methods • Important approach • Over-estimated by proponents, underestimated by opponents • Focus on null hypothesis tests is usually stupid • Better to measure size of effect • E.g. Glebbeek and Bax (2002) • Statistics = formal methods for doing induction • Sampling / context needs care • E.g. Glebbeek and Bax (2002) • “Qualitative” data often analysed statistically • Should be done properly

  20. Following a framework or paradigm • Neither hypothesis testing nor (pure) induction • Kuhn’s normal science • Obviously a good idea but not in the standard menu of approaches • More helpful concept than induction because focuses attention on the framework and presuppositions

  21. Hypotheses • Guiding vs formal • Formal hypotheses are tested. They may be • Null • Sadly, statistical null Hs tend to dominate idea of hypotheses. • Non-null. Popper’s bold conjectures. • Require imagination. Not boring! • Rigour is in the testing process • Guiding hypotheses are explored • Not restricted to statistical approaches. In fact hypotheses are usually best avoided with statistics

  22. Fictional “data” • Made up data may be more convenient • E.g. confidentiality problems • Thought experiments • Fictions, fables, utopias, dystopias to explore … • Widely used in mathematical modelling • Interesting possibilities fabricated to play out what-ifs • Sometimes what is possible may be more interesting than what has actually happened • E.g. if we are interested in improving things • Dogmatic empiricism may be unreasonable? If we want to change things why focus exclusively on facts?

  23. Does all this matter?We think so because … • Avoids impoverishing research by adhering to very restricted perspectives • Suggests new possibilities • Avoids wasting time talking rubbish • Fitting methods to the enquiry is important (e.g. check the two CRITIC acronyms in http://userweb.port.ac.uk/~woodm/rm/rm.ppt) • Fitting them to your favourite paradigm is not! • All comments and suggestions welcome. These slides and a revised draft paper are at • http://userweb.port.ac.uk/~woodm/QualQuant.ppt • http://userweb.port.ac.uk/~woodm/QualQuant.pdf

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