1. Single Subject/Small-N Designs

2. Traditional Group Design
Are widely used
Can address a broad range of questions
Requires a large number of homogenous participants that can be randomly assigned
Compares two or more groups in terms of effect on target behaviors

3. Single-Subject/ Small-N Designs
Permits experimental analysis with single or minimal subjects
Involves the study of one or more individuals across repeated observations before, during, and following the introduction of an intervention
Often look for trends and variability in graphed data

4. General Requirements for Small-N
Baseline Assessment
Continuous Assessment
Repeated Measures
Stability of Performance
Trend
Variability

5. Control Group Design
One person/group gets the intervention while the other doesn’t
Okay with large numbers but hard to say apples to apples with small numbers
Ethical issues withholding treatment. Ensure you have time to correct either group as needed with proper treatment

6. AB Design
Get baseline data without the intervention then implement the intervention and look for a change
Non-experiment design – next to impossible to control for variables
i.e. “How do you know the person wasn’t just going to get better anyway regardless of the intervention?”
Example – pre/post test design

7. ABAB Design
Get baseline data without the intervention then implement the intervention and look for a change. Take away the intervention to see if the subject(s) revert back to the baseline then add the intervention again to see if it has the same effect
Most important feature is that it includes a direct replication effect
NOTE = be careful of ethical issues taking away treatment

8. Criterion Design
Behavior improves in increments to match a criterion for performance
Several subphases are used in intervention
After the baseline, a criterion is set for behavior, and when behavior matches the criterion (and stabilizes) the criterion is changed
Common in behavior modification: sticker for hour, reward for day, reward for week
Disadvantage = gradual improvement not clearly connected to shifts in the criterion
Disadvantage = when there is a lack of withdrawl, it’s harder to determine the true role of the intervention

9. Multiple Treatment Design
After a stable baseline is established, two treatments are administered, alternating with each other
Treatments do not occur at the same time
Can be done with single subject, but stronger conclusions can be made when multiple subjects are used in different orders

10. Multiple Treatment Design
Jack intervention B
Jill intervention A
Jack intervention A
Jill intervention B

11. Multiple Baseline Design
AB Design but multiple subjects receive the intervention a increasingly delayed intervals

12. Reliability - Interobserver Agreement
Multiple people independently watch the same person(s) and record their findings
After the observation period, calculate the level of agreement between observers
Types of Calculations
Percentage of Agreement
Cohen’s Kappa
Intercorrelational coefficient
Generalizability using a two way ANOVA