Research strategies & Methods of data collection Experiment Observation

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?

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.

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)

Experiments The most „natural science like” method Less frequently used in economics (with the exception of 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

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

Major types Laboratory (lab) experiment Natural experiment

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

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

Biases from the participant side Placebo-effect Hawthore-effect

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 of the researchers Double blind experiments

Advantages Causality is measurable No need for representativeness Repeatability Inexpensive (relatively) Scientific rigour

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

Threats to internal validity history maturation Testing effect instrumentation Statistical regression Selection biases Experimental mortality demoralization

Threats to external validity It is not reality: even the pretest can change the situation. A possible solution: Solomon four group design

Preexperimental research designs Not real experiments There are three posible violations

Observation

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

Participant observation, researcher roles

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

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

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

Advantages of participant observation

Disadvantages of participant observation

Structured observation High levelof peetermined structure. Aim is to quantify behavior (how often? rather than why?).

Data collection The use of coding schedules

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

Advantages / disadvantages