Research strategies Methods of data collection Experiment Observation

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 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

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 • ’True’ and • ’quasi’-experiments

The classical experiment • Experimental and control groups formed • Experimental group: 1. Pretest 2. Stimulus 3. Posttest • Control group: 1. Pretest 2. No stimulus 3. Posttest

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 the researchers – Double blind experiments

Advantages • • • Causality is measurable No need for representativeness Relatively repeatabile 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 (see the next slide)

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

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 interpretation) • 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 level of predetermined 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