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Quasi-Experimental Designs

Quasi-Experimental Designs. Quasi-Experimental Designs. Intermediate between correlational study and true experiment. More than a relationship between variables. Low internal validity = cannot determine causality.

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Quasi-Experimental Designs

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  1. Quasi-Experimental Designs

  2. Quasi-Experimental Designs • Intermediate between correlational study and true experiment. • More than a relationship between variables. • Low internal validity = cannot determine causality. • In true experiment, IV is manipulated and subjects are randomly assigned to conditions. • In quasi-experiments, IV is “manipulated”, but subjects are already part of a group based on pre-existing characteristics.

  3. Nonmanipulated IV • IV occurs naturally • Participants are not randomly assigned to conditions. • Compares performance between 2 or more groups based on pre-existing characteristics. • Ex: gender; religion; age; smokers vs. nonsmokers; high, medium or low cholesterol levels. • Groups are not equivalent before treatment. • Low internal validity – we cannot conclude causality • Nonmanipulated independent variable and measure a particular dependent variable.

  4. Control group & Nonequivalent group • True experimental designs have an experimental group (treatment) and a control group (no treatment). • Participants are randomly assigned to either condition. • Quasi-experimental designs do not have a control group because there is no random assignment of participants to the conditions. • The nonequivalent group serves as the comparison to the treatment group

  5. Typical quasi-experimental design • Select 2 groups based on pre-existing characteristics. • Divide each group in half: half of the participants in each group get the treatment and half do not. • Compare performance with and without IV within each group and across groups. • Disadvantage • Pre-existing differences can confound results.

  6. Nonequivalent group design Age Males Females Caffeine Yes NO DV: # of anagrams solved

  7. Nonequivalent group design Age Young Old Memory Test RecallRecognition DV: % of words remembered

  8. Single-Case Experimental Designs

  9. Single case experimental designs • Involves the study of only 1 participant (single case designs) or 2 or 3 participants (small- n designs) • Often used in clinical settings. • Do not allow for generalization. • Allow for replications with different IV on the same participant or small-n designs. • Do not compare means nor run statistical analyses. • Assess how performance changes from one condition to another by graphing it.

  10. Baseline measurement • A measurement of behavior made under normal conditions (e.g., no IV is present); a control condition. • Serves to compare the behavior as affected by the IV. • Collect enough measures to achieve a stable pattern.

  11. Reversal Designs IV is introduced and removed one or more times. 1) A-B design - simplest of all designs - measure baseline behavior, apply treatment and compare behavior after treatment to baseline. - does not allow to establish cause-effect Representative Single-Case Experimental Designs

  12. A-B design treatment Behavior during/ after treatment Behavior at Baseline

  13. A-B-A design • Baseline measurement • Apply treatment • Measure change in behavior (posttest 1) • Remove treatment • Behavior “should” go back to baseline (final assessment)

  14. A-B-A design treatment Behavior with treatment Behavior at Baseline Remove treatment Behavior back to Baseline

  15. A-B-A-B design • Baseline measurement • Apply treatment • Measure change in behavior (posttest 1) • Remove treatment • Behavior “should” go back to baseline (assessment) • Apply treatment again • Measure change in behavior (posttest 2) • More ethical to end with treatment.

  16. A-B-A-B design Remove treatment treatment Behavior at Baseline Behavior with treatment treatment Behavior with treatment Behavior back to Baseline

  17. Multiple-Baseline Designs • Effects of IV are assessed across several participants, behaviors and situations. • Control for confounds by introducing treatment at different times for different participants, behaviors and situations.

  18. Multiple-baseline designs • Multiple-baseline across participants • Determine who has most stable baseline and introduce treatment to that subject first. • Multiple-baseline across behaviors • Determine most stable behavior and start with treatment on that behavior and then start on 2nd behavior. • Multiple-baseline across situations • Determine when behavior is occurring and tackle one situation at a time.

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