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1. Experiments and Quasi-Experiments. 2. Introduction. Experiment : using a controlled situation to observe a result Involves taking and observing action Great for hypothesis-testing Theory-full. 3. The Classical Experiment. Involves three major pairs of components: - PowerPoint PPT Presentation
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1
Experiments and Quasi-Experiments
Introduction
•Experiment: using a controlled situation to observe a result
•Involves taking and observing action
•Great for hypothesis-testing
•Theory-full
2
The Classical Experiment
•Involves three major pairs of components:
•Independent and dependent variables
•Pre-Testing and Post-Testing
•Experimental and Control groups
•Randomization
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Variables, X and Y
• X = Independent Variable (IV), cause, influencer
• Y = Dependent Variable (DV), effect, outcome
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Control and Experimental Groups
• Experimental group – exposed to whatever treatment, policy, initiative we are testing
• Control group – very similar to experimental group, except that they are NOT exposed
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Selecting Subjects
• Decide on target population 1st– the group to which the results of your experiment will apply
• Cardinal rule – ensure that C and E groups are as similar as possible
• Randomization helps towards this
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Hawthorne Effect• Pointed to the necessity of control groups
• IV: improved working conditions (better lighting)
• DV: improvement in employee satisfaction and productivity
• Workers were responding more to the attention than to the improved working conditions
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Placebo
• We often don’t want people to know if they are receiving treatment or not
• We expose our control group to a “dummy” IV just so we are treating everyone the same
• Medical research: participants don’t know what they are taking
• Ensures that changes in DV actually result from IV and are not psychologically based
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Pre-Testing and Post-Testing
• First, subjects measured on DV prior to association with the IV (pre-tested)
• Next, subjects are exposed to the IV
• Third, subjects are remeasured in terms of the DV (post-tested)
• Difference?--must be the intervention!
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Double-Blind Experiment
• Subjects and experimenters do not know who is in the control and experimental groups
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Experiments and Causal Inference• Experimental design ensures:
• Cause precedes effect via taking posttest
• Empirical correlation exists via comparing pretest to posttest
• No spurious 3rd variable influencing correlation via posttest comparison between experimental and control groups, and via randomization
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Internal Validity Threats (12)• Conclusions drawn from experimental results
may not reflect what went on in experiment
1. History – external events may occur during the course of the experiment
2. Maturation – people grow
3. Testing – the process of testing and retesting
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More Internal Validity Threats4. Instrumentation – Changes in the
measurement process
5. Statistical regression – Extreme scores regress to the mean
6. Selection bias – the way in which subjects are chosen
7. Experimental mortality – subjects may drop out prior to completion of experiment
8. Causal time order – ambiguity about order of stimulus and DV – which caused which?
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Last, Internal Validity Threats9. Diffusion/imitation of treatment – when E and C
groups communicate, E group may pass on elements to C
10. Compensatory treatment – C group is deprived of something considered to be of value
11. Compensatory Rivalry – C group deprived of the stimulus may try to compensate by working harder
12. Demoralization – feelings of deprivation result in C group giving up
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Construct Validity Threats
• Concerned with generalizing from experiment to actual causal processes in the real world
• Link construct and measures to theory
• Clearly indicate what constructs are represented by what measures
• Decide how much treatment is required to produce change in DV
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External Validity Threats
• Significant for experiments conducted under carefully controlled conditions rather than more natural conditions
• But, this reduces internal validity threats!
• A conundrum!
• Suggestion – explanatory studies -> internal validity; applied studies -> external validity
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Statistical Conclusion Validity Threats (Low Power)
• Problem is likely when using small samples
• With more cases, it is easier to see more differences
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Quasi-Experimental Designs
• When?—randomization not possible
• Quasi = “to a certain degree” or, in short, “like”
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