Types of Experiments & Research Designs

1. Types of Experiments & Research Designs UAPP 702: Research Design for Urban & Public Policy Based on notes from Steven W. Peuquet. Ph.D.

2. Types of Experiments There are four kinds of experiments: • True experiments • Quasi-experiments • Natural experiments • Naturalistic experiments

3. True Experiments • Some true experiments are done in the lab, others are done in the field. • There are five steps to follow in conducting true experiments:

4. True Experiments 1. Need at least two groups: anexperimental group and a control group. One groups gets the intervention, the other group does not. 2. Random assignment of individuals to the groups. (The degree to which random assignment ensures equivalence of the groups is dependant upon the size of the groups.)

5. True Experiments 3. The groups are measured on one or more dependent variables. This is called the pretest. 4. The intervention (independent variable) is introduced. 5. The dependent variables are measured again. This is the post test.

6. Quasi-Experiments • Are experiments where the ability to randomly assign individuals to the experimental and control groups is limited or nonexistent. • Quasi-experiments are more commonly used in evaluating social programs.

7. Quasi-Experiments Example In a study of the effectiveness of a new math curriculum, a 6th grade class in Porter Middle School was not given the new curriculum (the “control group”), while the 6th grade class in Farley Middle School was given the new curriculum (experimental group).

8. Quasi-Experiments Example (cont.) At the end of the school year the 6th graders in the Farley MS class had higher average math achievement test scores than the kids in the Porter MS 6th grade class. Is this a “quasi-experiment”? Why?

9. Quasi-Experiments Example (cont.) Answer: Most likely -- YES Random assignment to the two classes was probably not made, hence, groups may not be equivalent. Possibility of results being confounded by socio-economic status (SES) or other factors.

10. Natural Experiments Natural experiments are happening around us all the time. They are not conducted by researchers, but simply evaluated by researchers. In other words, the researcher does not have control over the application of the treatment. This also means that there is no control over what groups receive the treatment and the composition of those groups.

11. Natural Experiments Examples: • Migration patterns of people — some people migrate, some don’t. • Some middle class Latino students go to college, some don’t. Note: Each of these situations tests something about human behavior, the question is: “What is being tested by what is going on?”

12. Naturalistic Experiments In naturalistic experiments, one contrives to collect experimental data under natural conditions. You make the data happen out in the natural world (not in the lab), and you evaluate the results.

13. Naturalistic Experiments The difference between a natural experiment and a naturalistic experiment is that the first just happens, the second must be contrived to happen.

14. Naturalistic Experiments Naturalistic experiments deviate from true experiments because group membership is not randomly assigned, and exogenous factors (confounding variables) are not controlled.

15. Naturalistic Experiments Examples: • Links needed to connect any two randomly selected people (passage of folders) • Patience of drivers behind a car that did not move soon after a red light turned green (independent variables: newness of car and dress of driver) • Matched pair testing to uncover discrimination

16. Internal and ExternalValidity of an Experiment

17. Internal Validity • High internal validity means that changes in the dependent variable were caused by—not merely related to or correlated with—the treatment. • High internal validity can be attained only in an ideal true experimental design, or when steps have been taken to eliminate confounding factors or influences.

18. Internal Validity Internal validity can be increased by: • Repeating an experiment many times, and by changing or adding independent variables; • Switching the control and experimental groups • Controlling confounding influences.

19. Internal Validity Controlled experiments have the virtue of producing results with high internal validity, but they have the liability of low external validity

20. External Validity External validity is the extent to which the cause and effect relationship can be generalized to other groups that were not part of the experiment.

21. External Validity External validity is critically important in policy and program evaluation research because we would like our research findings to be generalizable. If external validity is low, then the results are not generalizable.

22. Threats to Validity History confound — Any independent variable other than the treatment variable, that occurs between the pre-test and the post-test.

23. Threats to Validity Maturation confound — Refers to the fact that as the experiment is conducted, people are growing older, and are getting more experienced. (Problem in longitudinal studies in particular)

24. Threats to Validity Testing confound — Refers to different responses that an experimental subject may have to questions they are asked over and over again.

25. Threats to Validity Instrumentation confound — Results from changing measurement instruments. (Example: use of different people to collect data, if there is no inter-rate reliability, you have changed the measurement instrument.)

26. Threats to Validity Regression to the mean — Groups tend to increasingly resemble the mean over time.

27. Threats to Validity Selection bias confound — When self-selection by individuals into groups is possible, a selection bias can result.

28. Threats to Validity Diffusion of treatment confound — Occurs when a control group cannot be prevented from receiving the treatment in an experiment.

29. Examples Let’s look at two related but different research questions, and see what types of experiments would be appropriate for each.

30. Final Important Principle! NEVER USE A RESEARCH DESIGN OF LESS LOGICAL POWER WHEN ONE OF GREATER LOGICAL POWER IS FEASIBLE !