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 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: 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 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
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