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QUASI-EXPERIMENTS

QUASI-EXPERIMENTS. Compare subjects in different conditions on a DV Lacks one or more criteria for an experiment (cause, comparison, control) Interpreted like a correlational study . Why Do Quasi-Experiments?. Internal validity is a problem because the IV is not manipulated

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QUASI-EXPERIMENTS

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  1. QUASI-EXPERIMENTS • Compare subjects in different conditions on a DV • Lacks one or more criteria for an experiment (cause, comparison, control) • Interpreted like a correlational study

  2. Why Do Quasi-Experiments? • Internal validity is a problem because the IV is not manipulated • It may not be possible or ethical to manipulate the IV

  3. Ex Post Facto Designs • Compare groups that differ on a pre-existing variable (subject variable) • Examples: • gender • personality • presence of mental disorder

  4. One-Group Posttest Only Design • Scores are measured after a treatment for one group • The treatment is not manipulated, and there is no comparison

  5. One-Group Pretest-Posttest Design • Scores are measured before and after an event or treatment • The treatment is not manipulated

  6. Non-Equivalent Control Group Design • Compare a treatment group and a control group before and after the treatment • The groups are not randomly assigned

  7. Single Group Time Series Design • Scores are measured several times before and after an event or treatment • Better than Pretest-Posttest design, because you can tell whether the change is likely to be due to a random fluctuation

  8. Multiple Time Series Design • Scores for a treatment and control group are measured several times before and after • The control group is not randomly assigned • Combination of Nonequivalent Control Group and Interrupted Time Series

  9. Cross-Sectional Design • Compare different age groups at one point in time • The groups may differ on other variables, since age is not manipulated • cohort effects - generational history differences

  10. Longitudinal Design • Measure the same group at different ages • Time-consuming • Attrition can be a problem

  11. Cohort Design • Compare different age groups at different ages • Combination of longitudinal and cross-sectional

  12. Statistics for Quasi-Experiments • Choose from the same set of statistics used for experiments • Remember that you interpret differences like the study was correlational • Take into account number of conditions and whether variables are between or within subjects

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