Small N Designs
ABA Designs
Multiple Baseline Design
Changing Criterion Designs
Discrete Trials Designs
When to Use Large N and Small N Designs
What is a large N design?
A large N design compares the performance of
groups of subjects.
Introduction
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
What are aggregate effects?
Aggregate effects are the pooled findings from
many subjects.
Small N Designs
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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