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Research strategies & Methods of data collection
Experiment Observation
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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?
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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.
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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)
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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
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The classical experiment
The dependent variable and the independent variable are identified Pretesting and posttesting are conducted Experimental and control groups are given
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Major types Laboratory (lab) experiment Natural experiment
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The classical experiment
Experimental and control groups formed Experimental group: Pretest Stimulus Posttest Control group: No stimulus
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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
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Biases from the participant side
Placebo-effect Hawthore-effect
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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
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Advantages Causality is measurable No need for representativeness
Repeatability Inexpensive (relatively) Scientific rigour
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Disadvantages Artificial Natural experiments are rare
Loose connection with complex, real situations
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Threats to internal validity
history maturation Testing effect instrumentation Statistical regression Selection biases Experimental mortality demoralization
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Threats to external validity
It is not reality: even the pretest can change the situation. A possible solution: Solomon four group design
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Preexperimental research designs
Not real experiments There are three posible violations
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Observation
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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
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Participant observation, researcher roles
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Decision on role Purpose of research Status of the reseacher Time
Degree of feeling suited to be a participant Access Ethics
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Data collection Note making and recording Progressing data collection
Descriptive observation Narrative account Focused observation
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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.
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Advantages of participant observation
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Disadvantages of participant observation
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Structured observation
High levelof peetermined structure. Aim is to quantify behavior (how often? rather than why?).
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Data collection The use of coding schedules
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Data quality Informant error (not the normal output is observed)
Time error (untypical)
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Advantages / disadvantages
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