Quasi-Experimental Design

1. 10 Quasi-Experimental Design

2. 10.1 Foundations of Quasi-Experimental Design • “Quasi” means “sort of” • Quasi-experiments have: • A control group • A treatment (or program) group • Variables • Quasi-experiments do not have: • Random assignment to groups

3. 10-2 The Nonequivalent Groups Design • One of the most frequently used quasi-experimental designs • Looks just like a pretest-posttest design • Lacks random assignment to groups • As a result, the treatment and control groups may be different at the study’s start • Raises a selection threat to internal validity

4. 10.2a Reaching Cause-and-Effect Conclusions with the NEGD

5. 10.2a Plot of Pretest and Posttest Means for Possible Outcome 1

6. 10.2a Plot of Pretest and Posttest Means for Possible Outcome 2

7. 10.2a Plot of Pretest and Posttest Means for Possible Outcome 3

8. 10.2a Plot of Pretest and Posttest Means for Possible Outcome 4

9. 10.2a Plot of Pretest and Posttest Means for Possible Outcome 5

10. 10.3 The Regression-Discontinuity Design • A pretest- posttestprogram comparison- group quasi-experimentaldesign in which a cutoff criterion on the preprogram measure is the method of assignment to a group

11. 10.3a The Basic RD Design • Notation • C indicates that groups are assigned by means of a cutoff score on the premeasure • An O stands for the administration of a measure to a group. • An X depicts the implementation of a program • Each group is described on a single line

12. 10.3a Regression Line • A line that describes the relationship between two or more variables

13. 10.3b The Role of the Comparison Group in RD Designs

14. 10.3c The Internal Validity of the RD Design • In principle, then, the RD design is as strong in internal validity as its randomized experimental alternatives • In practice, however, the validity of the RD design depends directly on how well you can model the true pre-post relationship, certainly a serious statistical challenge

15. 10.3d Statistical Power and the RD Design • To achieve the same level of statistical accuracy, an RD design needs as much as 2.75 times the participants as a randomized experiment • Example: if a randomized experiment needs 100 participants to achieve a certain level of power, the RD design might need as many as 275

16. 10.3e Ethics and the RD Design • RD designs tend to be more ethical, because those who need a program or treatment the most can receive it

17. 10.4a The Proxy Pretest Design • A post-only design in which, after the fact, a pretest measure is constructed from preexisting data • Usually done to make up for the fact that the research did not include a true pretest

18. 10.4b The Separate Pre-Post Samples Design • A design in which the people who receive the pretest are not the same as the people who take the posttest

19. 10.4c The Double-Pretest Design • A design that includes two waves of measurement prior to the program • Addresses selection-maturation threats

20. 10.4d The Switching-Replications Design • A two-group design in two phases defined by three waves of measurement • In the repetition of the treatment, the two groups switch roles: • The original control group in phase 1 becomes the treatment group in phase 2, whereas the original treatment group acts as the control

21. 10.4e The Nonequivalent DependentVariables (NEDV) Design • At first, looks like a weak design • But pattern matching gives researchers a powerful tool for assessing causality • The degree of correspondence between two data items

22. 10.4f The Regression Point Displacement (RPD) Design • A pre-post quasi-experimental research design where the treatment is given to only one unit in the sample, with all remaining units acting as controls • This design is particularly useful to study the effects of community level interventions

23. 10.4f The Regression Point Displacement (RPD) Design Analyze with ANCOVA

24. Discuss and Debate • Why can quasi-experiments be more ethical than randomized experiments? • What are the strengths and the weaknesses of quasi-experimental designs?