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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 ([email protected]) 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

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 …

- Perspective 1
or

- Perspective 2
or

- Perspective 3
or

- Perspective 4
Which do you think will be the winner?

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

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.

- 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.

- 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?

Easterby-Smith et al (2002) list 8 bipolar dimensions: this leads to the possibility of 28 or 64 types of research.

- 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

- 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 …

Use different approaches in one project …

- 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

“Qualitative”

Statistical

Hypothetico-deductive

“Inductive”

“ …” indicates vague terms we don’t like

Deterministic laws

Empirical possibilities

X

“Qualitative”

Statistical

?

?

Fictional possibilities

Is this one 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

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 …

- 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 …

- 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

- 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

- 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

- Null
- Guiding hypotheses are explored
- Not restricted to statistical approaches. In fact hypotheses are usually best avoided with statistics

- 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?

- 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