1. Research methodology
2. Definition, Method, Data and Evaluation
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2. The research cycle
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3. First step: Problem formulation
• Typical problems (in applied empirical science...):
– explain something observed
• why did this happen
• what are the motives for
– evaluate effect of something
• new policy or new organisational form
– find out what characterise something
• description, propose a typology)
– analyse what is the best way to do something given certain goals
• but based on observed relations
– predictions
• must also be based on observed relations
• Very important: Is this a problem where it is possible to
get useful information?
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4. • Problem should be clearly formulated
– relations to problems discussed in literature
– relations to practical situation in firms/organisation...
– formulated in clear terms
• a clear term: A term where application is uncontroversial?
• special problem: Value loaded terms
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5. Definitions (1)
• Definitions never an end in itself
• meaning can be clear enough even if we cannot define the term..
• Define a term in two situations:
• term not known to the audience
• term given different interpretations by different people
• A good definition:
• uses terms that is clear and known by the audience!
• e.g. not circular
• Important to separate controversies related to facts from
controversies about whether a certain term can be applied
to these facts
• “what happened on the market” vs “is it correct to apply the term
bubble to this event”
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6. Definitions (2)
• Different kinds of definitions:
– Stipulative definitions (the usual ones in science)
• this is how I use the term
– Descriptive definitions
• this is how a certain group of people use the term
– Operational definitions
• relate to how measure
• Sometimes do not exist a set of necessary and sufficient
conditions for applying a certain term
– The idea of “family resemblance”: A network of overlapping
similarities characterise what falls under a term
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7. Next steps
• How to get data/information
• "Research design"
• Evaluation of data in relation to hypothesis/statement
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8. Data sources
• Existing data from registers, statistical authorities, etc
• Interviews
• Questionnaires
• What to choose:
– perhaps combination?
• How to design
– see special literature!
– logical order, clear terms, etc: check earlier theses
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9. Data quality
• Reliability
– would you get the same data of you replicated the
study/measurement
• interview answers depend on who asks
• result on questionnaire depend on exact formulation or when
questionnaire was made
– check in a systematic way..
• Validity
– does the data say anything about what we are interested in
• does registered unemployment say anything about who really is
unemployed
• are they telling the truth?
• What do we do with “outliers”?
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10. Research design
Control event (to some degree...)
• Classical experiment:
– randomly selected groups from same population
– one group “treated” in a certain way
• Experiment/test: What happens if.... :
– two conflicting theories
– implicit control group?
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11. Observe without any control.......
1. Quasi-experimental method
- try to find group that can be used as a control group, compare result
2. Collect data with the purpose of statistically identifying
relationships
3. Case study method
- try to identify possible relations and mechanisms in specific case,
comparisons
Not very clear borders between methods!
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12. How many observations do you need?
• See next part!
• Depend upon what you want to know
– small difference, large difference
– how it is, how it can be
– tendency...
• Generalizations never possible!
– at least according to some philosophers
• Certain knowledge not possible!
– at least according to some philosophers
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13. Evaluation of hypothesis in relation to evidence
• General perspective: Same situation as a judge that
evaluate evidence in order to make judgement
– very seldom conclusive evidence
• Is there a statistical relation (correlation)
– How likely is that we would observe a correlation if there is no
relation?
– Measures of statistical significance
– Depends upon
• number of observations
• how strong the relation is and how exact you want to measure it
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14. • From statistical relation to causal relation: Is this really the
cause?
– Is there a plausible mechanism relating cause and effect?
– Is the relation robust
• over time
• over space
• when we change the form of statistical function (linear, nonlinear)
• If there are competing plausible hypotheses that both fit the
facts?
– Try to find some situation where the hypotheses give different
implications? Collect data about this situation?
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15. • A bayesian approach: How probable is a certain
hypothesis?
– Start with apriori probability
• related to background theories, credibility of the person, competing
theories
– Collect new information
– Revise probabilities
• given an evaluation of the quality of the information
• how likely is the evidence given each competing hypothesis?
– strong support if unlikely given competing theories
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16. • Can explain why scientific changes usually takes time
• If “old” theories have high prior probability it takes a lot of
information to lead to a revision. Always problems with a specific
study.
• If difficult to get data competing theories can coexist for
long periods of time.
• Difference between social and natural science that both
stronger prior probabilities and more difficult to get good
data in social science?
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17. Concluding comment
• Important to have a consistent plan!
• Important to collect data that you know that you can draw
any conclusions from given the questions you are
interested in
• No "hard" data, no clear line between qualitative and
quantitative methods
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