1. Quasi-Experiments – Outline 1. True Experiments a. Characteristics b. Threats to validity controlled by experiments c. Threats not controlled by experiments d. Obstacles to true experiments in the field 2. Quasi-experiments a.The logic of quasi-experiments b.Non-equivalent control group design Example – Langer & Rudin (1976) c. Interrupted time-series design Example – Campbell (1969)  Quasi

2. True Experiments - Characteristics True experiments are characterized by: A manipulation A high degree of control An appropriate comparison (the major goal of exerting control) Manipulation in the presence of control gives you an appropriate comparison.  Quasi

3. Threats to validity controlled by true experiments History occurrence of an event other than the treatment  Quasi

4. Threats to validity controlled by true experiments Maturation participants always change as a function of time. Is change in behavior due to something else?  Quasi

5. Threats to validity controlled by true experiments Testing improvement due to practice on a test (familiarity with procedure, or with testers expectations)  Quasi

6. Threats to validity controlled by true experiments Instrumentation especially if humans are used to assess behavior (fatigue, practice)  Quasi

7. Threats to validity controlled by true experiments Regression when first observation is extreme, next one is likely to be closer to the mean.  Quasi

8. Threats to validity controlled by true experiments Selection if differences between groups exist from the outset of a study  Quasi

9. Threats to validity controlled by true experiments Mortality if exit from a study is not random, groups may end up very different  Quasi

10. Threats to validity controlled by true experiments Interactions of selection… with History with Maturation with Instrumentation (ceiling effects)  Quasi

11. Note difference between these threats: Maturation One group; performance better on post-test than on pre-test Interaction of Maturation & Selection Two or more groups Performance difference larger on post-test than on pre-test  Quasi

12. Threats to validity not controlled by experiments Contamination communication of information about the experiment between groups of subjects Cook & Campbell (1979): resentment ‘compensatory rivalry’ diffusion of treatment: control subjects use information given to others to change their own behavior.  Quasi

13. Contamination – an example Craven, Marsh, Debus, & Jayasinghe (2001) Journal of Educational Psychology Teachers trained to improve students’ academic self-concept through praise Internal control External control  Quasi

14. Contamination – an example Craven, Marsh, Debus, & Jayasinghe (2001) Next slide shows T2 (post- test) academic self- concept scores as a function of T1 scores for control children only.  Quasi

15. External control Internal control Internal high focus Internal low focus 1.0 0.5 0.0 -0.5 LowMediumHigh T1 acad self concept Diffusion No diffusion T2 acad self concept Low focus group consistently higher than external control Resentful demoralization? Overzealous cooperation?

16. Threats to validity not controlled by experiments Threats to external validity best way to deal with this is replication  Quasi

17. Threats to validity not controlled by experiments Hawthorne effects changes in a person’s behavior due to being studied rather than the manipulation. a special kind of reactivity.  Quasi

18. Hawthorne effects Demand characteristics cues communicated by researcher subject’s under-standing of their role  Quasi

19. Hawthorne effects Role of “research subject” Is subject behaving the way he thinks a person in that role should behave? (E.g., hypnotized person)  Quasi

20. Hawthorne effects Orne (1962) ‘good subjects’ think they are contributing to science by complying with researcher’s demands  Quasi

21. Hawthorne effects What to do about Hawthorne effects? Orne (1962): Use quasi- control subjects as “co- investigators” They do your task, reflect on demand characteristics of the experiment.  Quasi

22. Obstacles to true experiments in the field Sometimes, we cannot bring the phenomenon we want to study into the lab, so we have to work in the field. Can we do experiments in the field?  Quasi

23. Obstacles to true experiments in the field Can’t get permission from individuals in authority? Your study may involve some time and effort on their part. But what’s in it for them? In schools, parents also have to agree.  Quasi

24. Obstacles to true experiments in the field Can’t assign subjects to groups randomly? have to work with intact groups (e.g., classes in a school)  Quasi

25. Quasi-Experiments Quasi-experiments resemble true experiments… usually include a manipulation, and provide a comparison. …but they are not true experiments. lack high degree of control that is characteristic of true experiments.  Quasi

26. Quasi-Experiments Quasi-Experiments are compromises They allow the researcher some control when full control is not possible.  Quasi

27. Quasi-Experiments Because full control is not possible, there may be several “rival hypotheses” competing as accounts of any change in behavior observed. How do we convince others that our hypothesis is the right one?  Quasi

28. The Logic of Quasi-Experiments Eliminate any threats you can Show how each threat to validity on list given above is dealt with in your study. Argue that others don’t apply. using evidence or logic  Quasi

29. Two kinds of quasi-experiments Non-equivalent control group “non-equivalent” because not randomly assigned  Quasi

30. Two kinds of quasi-experiments Interrupted time-series design a series of observations over time, interrupted by some treatment  Quasi

31. Non-equivalent Control Group design Control group is “like” the treatment group. Chosen from same population Pre- and post-test measures obtained for both groups, so similarity can be assessed.  Quasi

32. Non-equivalent Control Group design Control group is not equivalent subjects are not randomly- assigned to control & treatment groups so best you can do is argue that comparison is appropriate.  Quasi

33. Non-equivalent Control Group design If the groups are comparable to begin with, this design potentially eliminates threats to internal validity due to: History Maturation Testing Instrumentation Regression  Quasi

34. Problems with the NECG design Threats to validity due to interactions with selection may not be eliminated using the NECG design. Selection and maturation Most likely when treatment group is self-selected (as in psychotherapy cases – people who sought help).  Quasi

35. Problems with the NECG design Selection and history Does one group experience some event that has a positive or negative effect (e.g., teacher of one class leaves)? Quasi

36. Problems with the NECG design Selection and instrumentation Does one group show ceiling or floor effects? Quasi

37. Problems with the NECG design Regression to the mean Are one group’s pretest scores more extreme than the other group’s? Quasi

38. Possible NECG study outcomes both experimental and control groups show improve-ment from pretest to posttest appears not to be any effect of the treatment Quasi Pretest Posttest Control group

39. Possible NECG study outcomes Looks like a treatment effect, but there may be a threat due to s election and maturation, selection and history  Quasi PretestPosttest Control group

40. Possible NECG study outcomes Selection and maturation could be a threat Or interaction of selection and history testing instrumentation or mortality. Quasi PretestPosttest Control group

41. Possible NECG study outcomes Interaction of selection and regression looks like a serious threat here Selection and maturation probably not a threat here. Quasi PretestPosttest

42. Possible NECG study outcomes Crossover effect Clearest evidence for an effect of the program of any of these graphs. Selection and instrumentation not a problem – no ceiling or floor effects Quasi PretestPosttest

43. Quasi-experiment example Langer & Rudin (1976) Research conducted in retirement home. Residents on one floor given more control over their daily lives Residents of another floor given same interaction with staff, but no increased control. Quasi

44. Langer & Rudin (1976) – Measures Ratings Self-report of feeling of control from residents Staff assessments of mental & physical well-being, by ‘blind’ assessors Objective measures record of movie attendance participation in “Guess how many jelly-beans” contest on each floor Quasi

45. L & R (1976) – limits on control L & R had no control over who entered the home who was assigned to either floor. no control over staff hiring or firing / resigning. Quasi

46. L & R (1976) – Possible Problems Interaction of Selection and Maturation even if groups have similar pretest scores, they may differ on things pretest didn’t measure probably not a problem here – people on both floors had similar SES assigned to floors randomly, not by health status. Quasi

47. L & R (1976) – Possible Problems Selection and history suppose a popular (or unpopular) nurse left one of the floors during the study. That might influence well- being. L & R did not address this issue. Quasi

48. L & R (1976) – Possible Problems Selection and instrumentation did one group show ceiling or floor effects? L & R say, no. Quasi

49. L & R (1976) – Possible Problems Regression were one group’s pretest scores more extreme than the others? L & R say, no.  Quasi

50. L & R (1976) – Possible Problems Observer bias and Contamination observers in the L & R study were not aware of the hypothesis. L & R reported there was little communication between floors. Quasi

51. L & R (1976) – Possible Problems Hawthorne Effect cannot be ruled out, but L & R took care to give both floors same attention. Message varied between floors, but “face time” was the same.  Quasi

52. L & R (1976) – Possible Problems External Validity might be an issue. home involved was rated “one of the finest” in the state subjects may have been atypical in their desire for control  Quasi

53. Two kinds of quasi-experiments Non-equivalent control group Interrupted time-series design a series of observations over time, interrupted by some treatment  Quasi

54. Time-Series Designs In T-S designs, performance is measured both before and after a treatment. If there is an abrupt change in performance at time of treatment, we conclude that treatment worked.  Quasi

55. Time-series designs example Campbell (1969) Effect of speed limit reduction on traffic fatalities in Connecticut incidence of traffic fatalities in years before and after the speed limit reduction, conclusion: speed limit change had a modest effect.  Quasi

56. Campbell (1969) Any threat to internal validity? other explanations for any change in traffic fatality incidence: Changes in car safety Weather Record keeping  Quasi

57. Campbell (1969) Any threat to internal validity? Such effects should be similar in neighboring states Campbell found no change in fatality incidence in those states.  Quasi

58. Campbell (1969) Any threat to external validity? E.g., would treatment have same effect in other states, or are people in Connecticut more law- abiding?  Quasi

59. Campbell (1969) Time-series design eliminates most other threats to validity – e.g., maturation, testing, regression. For example, maturation would probably not produce a sudden change in performance of the kind found in Time-Series Designs.  Quasi