Experiments and Quasi-Experiments - PowerPoint PPT Presentation

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Experiments and Quasi-Experiments
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Experiments and Quasi-Experiments

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  1. 1 Experiments and Quasi-Experiments

  2. 2 Introduction • Experiment: using a controlled situation to observe a result • Involves taking and observing action • Great for hypothesis-testing • Theory-full

  3. 3 The Classical Experiment • Involves three major pairs of components: • Independent and dependent variables • Pre-Testing and Post-Testing • Experimental and Control groups • Randomization

  4. 4 Variables, X and Y • X = Independent Variable (IV), cause, influencer • Y = Dependent Variable (DV), effect, outcome

  5. 5 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

  6. 6 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

  7. 7 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

  8. 8 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

  9. 9 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!

  10. 10 Double-Blind Experiment • Subjects and experimenters do not know who is in the control and experimental groups

  11. 11 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

  12. 12 • Conclusions drawn from experimental results may not reflect what went on in experiment • History – external events may occur during the course of the experiment • Maturation – people grow • Testing – the process of testing and retesting Internal Validity Threats (12)

  13. 13 4. 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? More Internal Validity Threats

  14. 14 9. 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 Last, Internal Validity Threats

  15. 15 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

  16. 16 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

  17. 17 Statistical Conclusion Validity Threats (Low Power) • Problem is likely when using small samples • With more cases, it is easier to see more differences

  18. 18 Quasi-Experimental Designs • When?—randomization not possible • Quasi = “to a certain degree” or, in short, “like”