1. 9
Experimental Design

2. 9.1 Foundations of Experimental Design
Experiments have the strongest internal validity due to:
Random assignments to a group
What is random assignment?
Random assignment is the process of assigning your sample into two or more subgroups by chance

3. 9.2 Introduction: The Origins of Experimental Design
Sir Ronald Fisher: credited with the invention of the experiment
Edward L. Thorndike & Robert S. Woodworth: identified the need for control groups
Sir Austin Bradford Hill: conducted the first clinical trial
Jonas Salk: used the clinical trial to test polio vaccine

4. 9.2a Distinguishing Features of Experimental Design
Program group
Treatment group
Comparison group
Control group
Probabilistically equivalent
Program group: In a comparative research design, like an experimental or quasi-experimental design, the program or treatment group receives the program of interest and is usually contrasted with a no-treatment comparison or control group or a group receiving another treatment.
Treatment group: In a comparative research design, like an experimental or quasi-experimental design, the program or treatment group receives the program of interest and is usually contrasted with a no-treatment comparison or control group or a group receiving another treatment.
Comparison group: In a comparative research design, like an experimental or quasi-experimental design, the control or comparison group is a group that is compared or contrasted with a group that receives the program or intervention of interest.
Control group: In a comparative research design, like an experimental or quasi-experimental design, the control or comparison group is a group that is compared or contrasted with a group that receives the program or intervention of interest.
Probabilistically equivalent: The notion that two groups, if measured infinitely, would on average perform identically. Note that two groups that are probabilistically equivalent would seldom obtain the exact same average score.

5. 9.2b Experimental Design and Threats to Internal Validity
9.5 Threats to internal validity for the posttest-only, randomized experimental design.

6. 9.2c Design Notation for a Two-Group Experimental Design
Figure 9.6 Notation for the basic two-group posttest-only randomized experimental design.
Analyze differences using a t-test or ANOVA.

7. 9.2d The Difference Between Random Selection and Assignment
Random selection is how you draw the sample of people for your study from a population
Random assignment is how you assign the sample that you draw to different groups or treatments in your study

8. 9.3 Classifying Experimental Designs
Signal-enhancing designs
Factorial designs
Noise-reducing designs
Covariance designs
Blocking designs

9. 9.4 Signal Enhancing Designs: Factorial Designs
Designs that focus on the program or treatment, its components, and its major dimensions, and enable you to determine whether the program has an effect, whether different subcomponents are effective, and whether there are interactions in the effects caused by subcomponents

10. 9.4a The Basic 2 x 2 Factorial Design
Figure 9.9 An example of a basic factorial design.
Factor: A major independent variable.
Level: A subdivision of a factor into components or features.
Figure 9.10 Design notation for a 2 x 2 factorial design

11. 9.4a The Basic 2 x 2 Factorial Design: Possible Outcome
Figure 9.11 The null effects case in a 2 x 2 factorial design.

12. 9.4a A Main Effect of Time in Instruction in a 2 x 2 Factorial design
Figure 9.12 A main effect of time in instruction in a 2 x 2 factorial design.

13. 9.4a A Main Effect of Setting in a 2 3 2 Factorial Design
Figure 9.13 A main effect of setting in a 2 x 2 factorial design.

14. 9.4a Main Effects of Both Time and Setting in a 2 x 2 Factorial Design
Figure 9.14 Main effects of both time and setting in a 2 x 2 factorial design.

15. 9.4a An Interaction in a 2 x 2 Factorial Design
Figure 9.15 An interaction in a 2 x 2 factorial design

16. 9.4a A Crossover Interaction in a 2 x 2 Factorial Design
Figure 9.16 A crossover interaction in a 2 x 2 factorial design.

17. 9.4b Benefits and Limitations of Factorial Designs
Enhances the signal
Efficient design
Only design that allows you to examine interactions
Limitations
Complex
More participants required

18. 9.4c Factorial Design Variations: 2 x 3
Figure 9.17 Main effect of setting in a 2 x 3 factorial design.

19. 9.4c Factorial Design Variations: 2 x 2 x 3
Figure 9.21 Example of a 2 x 2 x 3 factorial design

20. 9.4c Factorial Design Variations: Incomplete Design
Figure 9.23 An incomplete factorial design

21. 9.5 Noise-Reducing Designs: Randomized Block Designs
Helps minimize noise through the grouping of units (e.g., participants) into one or more classifications (blocks) that account for some of the variability in the outcome
Figure 9.24 The basic randomized block design with four groups or blocks.

22. 9.6 Noise-Reducing Designs Covariance Designs
Helps minimize noise through the inclusion of one or more variables (covariates) that account for some of the variability in the outcome measure or dependent variable
Covariates are the variables you adjust for in your study
Figure 9.25 Notation for the basic analysis of covariance design.

23. 9.7 Hybrid Designs: Switching-Replications Experimental Designs
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
Figure 9.26 Notation for the switching-replications randomized experimental design.

24. 9.7 Hybrid Designs: Switching-Replications – Short Term Effect
Figure 9.27 Switching- Replications design with a short-term persistent treatment effect

25. 9.7 Hybrid Designs: Switching-Replications – Long Term Effect
Figure 9.28 Switching-Replications design with a long-term continuing treatment effect. Group 1 received the treatment first, then Group 2.

26. 9.8 Limitations of Experimental Design
Differential drop out (mortality threat)
Ethical problems
Social threats to internal validity
Difficult to generalize to the real world

27. Discuss and Debate
What is the difference between random selection and random assignment?
What are some strengths and weaknesses of experimental designs?
Can you think of some research topics for which a factorial design may be a good approach?
Random selection is the process of randomly drawing a sample from the population. Random assignment, on the other hand, involves taking the sample the researcher has drawn, and then randomly assigning each unit (participant) to either the treatment group or the control group.
Experiments are often considered to be “the gold standard” in social science research, but they are also criticized for being artificial. What is observed under tightly controlled conditions may not be what happens in the natural environment. Experiments also bring up a host of ethical concerns, which must be carefully addressed. Other research designs may be appropriate, such as a quasi-experimental design, or a qualitative approach.
Any time the researcher wishes to enhance the “signal” in a construct, a factorial design is a good strategy to use. In addition, factorial designs allow researchers to expand the number of independent variables in a study, as well as add levels to those variables. Ask your students to provide some real-world examples (e.g., spending $100 or $200 at the mall versus online).