1. Small N Designs
ABA Designs
Multiple Baseline Design
Changing Criterion Designs
Discrete Trials Designs
When to Use Large N and Small N Designs

2. Introduction What is a large N design?
A large N design compares the performance of groups of subjects.
Introduction

3. How does a large N design differ from a small N design?
A small N design studies one or two subjects, often using variations of the ABA reversal design.
Small N Designs

4. What are aggregate effects?
Aggregate effects are the pooled findings from many subjects.
Small N Designs

5. Why do small N researchers challenge large N experiments?
They argue that large N studies ignore individual subject responses to the IV and instead report aggregate results or trends. When subjects vary greatly in their response to the IV, this can create the appearance of no difference between the groups.
Small N Designs

6. Why might a clinical psychologist use small N designs?
A clinical psychologist could use a small N design to test a treatment when there are insufficient subjects to conduct a large N study and when she wants to avoid the ethical problem of an untreated control group.
Small N Designs

7. How about an animal researcher?
Animal researchers prefer small N designs to minimize the acquisition and maintenance cost, training time, and possible sacrifice of their animal subjects.
Small N Designs

8. Which historical development caused the shift to large N designs?
Sir Ronald Fisher’s (1935) creation of the analysis of variance allowed inferential testing of large N data.
Small N Designs

9. Where have small N designs been most extensively used?
Small N designs have been most extensively used in operant conditioning research.
B. F. Skinner examined the continuous behavior of individual subjects in preference to analyzing discrete measurements from separate groups of subjects.
Small N Designs

10. Explain the function of a baseline.
In both large and small N designs, baselines are control conditions that allow us to measure behavior without the influence of the IV.
ABA Designs

11. How did Kazdin explain the decision of many clinical researchers to end without a return to baseline?
It would be ethically indefensible to cause a patient to relapse by returning to baseline after treatment appeared to improve behavior.
ABA Designs

12. When is this most important?
When relapse threatens the health or safety of the patient or others, as in self-injurious, and suicidal or homicidal behavior.
ABA Designs

13. What price do researchers pay when they can't return to baseline?
They can’t rule out the possibility that the patient’s clinical improvement was caused by an extraneous variable.
ABA Designs

14. What is a multiple baseline design?
In a multiple baseline design, a series of baselines and treatments are compared within the same subject, and once treatments are administered, they are not withdrawn.
Multiple Baseline Design

15. What is a multiple baseline design?
This approach could also be used to evaluate the effect of a treatment administered to different individuals after baselines of different lengths.
A researcher can evaluate the effects of a treatment on two or more behaviors or on the same behavior in different settings.
Multiple Baseline Design

16. Multiple Baseline Design
How might this design overcome the ethical problem of withdrawing an effective treatment?
In a multiple baseline design, an experimenter never withdraws treatments after administering them.
Multiple Baseline Design

17. How do researchers analyze data from small N experiments?
Researchers often visually inspect changes in the dependent variable across treatment conditions. The independent variable’s effect is often apparent.
They may also use statistics to analyze small N data.
Statistics and Variability in Small N Designs

18. Why is statistical analysis of small N data controversial?
Critics are concerned about generalizing from a single subject to a population. Unless 50 measurements are taken during each baseline and treatment phase, important assumptions underlying inferential tests may be violated.
Statistics and Variability in Small N Designs

19. What are changing criterion designs?
In changing criterion designs, the criteria for reinforcement are incrementally increased as participants succeed. For example, initially, a subject might receive a reward for 30 minutes of daily exercise, later, for 45 minutes, and finally, for 60 minutes.
Changing Criterion Designs

20. What are changing criterion designs?
Reinforcement for successive approximations of the target behavior is central to athletic training, behavior modification, and biofeedback and neurofeedback.
Changing Criterion Designs

21. Explain a discrete trials design.
A discrete trials design is a small N design without baselines used in psychophysical research.
Instead, the impact of different levels of the independent variable is averaged across 100s to 1000s of trials
Discrete Trials Designs

22. How does a discrete trials design differ from a typical experiment?
A discrete trials design has no baselines and administers the levels of the independent variable 100s to 1000s of times to each subject.
Discrete Trials Designs

23. What are a discrete trials design’s benefits?
The large number of data points produced by 100s to 1000s of trials provides a very reliable measurement of the effect of the independent variable.
The similarity of human sensory systems allows researchers to generalize from a small number of subjects.
Discrete Trials Designs

24. When is a small N design appropriate?
When studying a clinical subject (a self-injurious child) or when very few subjects are available.
When to use Large N and Small N Designs

25. When would we prefer a large N design?
A large N design would be desirable when we have sufficient subjects and want to increase generalizability.
The generalizability of a large N study depends on how we select our sample since a seriously biased sample will not represent the population.
When to use Large N and Small N Designs

26. When would we prefer a large N design?
The generalizability of a small N study depends on repeated successful replications with different subjects.
When to use Large N and Small N Designs

27. When to use Large N and Small N Designs
Why doesn't a large N study always have greater generality than a small N study?
If a large N study’s sample is biased, we will be unable to generalize its findings to a larger population. Also, if it is poorly controlled, there will be no valid findings to generalize.
When to use Large N and Small N Designs

28. When to use Large N and Small N Designs
Why doesn't a large N study always have greater generality than a small N study?
In contrast, a well-controlled small N experiment using a single subject might be successfully replicated across sufficient subjects to generalize its results to the population from which they were drawn.
When to use Large N and Small N Designs