1. Research strategies & Methods of data collection
Experiment
Observation

2. Significance
Statistical significance: the measured effect or connection etc. is likely to truly exist (it is not likely to be the consequence of randomness).
Practical significance: the effect is big enough or the connection is strong enough to be practically important?

3. Significance Type I error: rejecting a null hypothesis that is true
The significance level α is the probability of making the wrong decision when the null hypothesis is true.
Type II error: failing to reject the null hypothesis when it should be rejected.

4. Ways of investigation (research strategies)
Choosing a research strategy:
Experiment
Survey
Archival and documentary research
Case study (!)
Action research (emergent and iterative; solutions to real problems; participative&collaborative; mixed knowledge)
Grounded theory (reality is socially constructed; developing explanations to social interactions; inductive/abductive)

6. Experiments The most „natural science like” method
Less frequently used in economics (with the exception of behavioral and experimental economics), but is fairly accepted in management
The idea is: if everything is kept constant or under control except the one experimental stimulus, than causality can be identified and its impact measured

7. The classical experiment
The dependent variable and the independent variable are identified
Pretesting and posttesting are conducted
Experimental and control groups are given

8. Major types Laboratory (lab) experiment Natural experiment ’True’ and
’quasi’-experiments

9. The classical experiment
Experimental and control groups formed
Experimental group:
Pretest
Stimulus
Posttest
Control group:
No stimulus

10. Assumptions of the classical experiment
The control and the experiental group are identical (as similar as possible). Ways to accomplish:
Probability sampling
Randomization
Matching
No other impact should be on the groups
No bias from the researcher or from the participants

11. Biases from the participant side
Placebo-effect
Hawthore-effect

12. Researcher bias Biased perception based on expectations
Ways to avoid this:
Rigorous and strict operationalization
More objective measurement methods
Measurement is based on tools and machines
Training the researchers
Double blind experiments

13. Advantages Causality is measurable No need for representativeness
Relatively repeatabile
Inexpensive (relatively)
Scientific rigour

14. Disadvantages Artificial Natural experiments are rare
Loose connection with complex, real situations

15. Threats to internal validity
History
Maturation
Testing effect
Instrumentation
Statistical regression
Selection biases
Experimental mortality
Demoralization

16. Threats to external validity
It is not reality: even the pretest can change the situation. A possible solution: Solomon four group design (see the next slide)

18. Pre-experimental research designs
Not real experiments
There are three posible violations (see the next slide)

20. Observation

21. Definition
Systematic viewing, recording, description, analsys and interpretation of behavior and/or processes
Two traditional types:
Participant observation
Structured observation
Two new, additional types:
Internet mediated observation
Videography

22. Participant observation, researcher roles

23. Decision on role Purpose of research Status of the reseacher Time
Degree of feeling suited to be a participant
Access
Ethics

24. Data collection Note making and recording Progressing data collection
Descriptive observation
Narrative account
Focused observation

26. Data quality
Observer error (misinterpreting), observer drift (changing interpretation)
Observer bias (subjective view) Informant verification can decrease this bias.
Observer effect. Minimal interaction, habituation can help.

27. Advantages of participant observation

28. Disadvantages of participant observation

29. Structured observation
High level of predetermined structure.
Aim is to quantify behavior (how often? rather than why?).

30. Data collection
The use of coding schedules

31. Data quality Informant error (not the normal output is observed)
Time error (untypical)

32. Advantages / disadvantages