CH 12

• Small N Designs


  Studies one or two subjects, often using variations of the ABA reversal design
• ABA Designs


  Baselines are control conditions that allow us to measure behavior without the influence of the IV
• Multiple Baseline Design


  A series of baselines and treatments are compared within the same subject, and once treatments are administered, they are not withdrawn
• Changing Criterion Designs


  The criteria for reinforcement are incrementally increased as participants succeed
• Discrete Trials Designs


  A small N design without baselines used in psychophysical research, where the impact of different levels of the IV is averaged across 100s to 1000s of trials
• When to Use Large N and Small N Designs

  1. When studying a clinical subject or when very few subjects are available

  2. When we have sufficient subjects and want to increase generalizability
• Large N design


  Compares the performance of groups of subjects
• How does a large N design differ from a small N design?
• Aggregate effects


  The pooled findings from many subjects
• 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
• Why might a clinical psychologist use small N designs?

  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
• Why might an animal researcher use small N designs?

  To minimize the acquisition and maintenance cost, training time, and possible sacrifice of their animal subjects
• What historical development caused the shift to large N designs?
• Where have small N designs been most extensively used?
• Why 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
• 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
• 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
• How might a multiple baseline design overcome the ethical problem of withdrawing an effective treatment?
• How do researchers analyze data from small N experiments?

  Researchers often visually inspect changes in the dependent variable across treatment conditions, and may also use statistics to analyze small N data
• Why is statistical analysis of small N data controversial?

  Critics are concerned about generalizing from a single subject to a population, and unless 50 measurements are taken during each baseline and treatment phase, important assumptions underlying inferential tests may be violated
• Reinforcement for successive approximations of the target behavior is central to

  Athletic training, behavior modification, and biofeedback and neurofeedback
• How does a discrete trials design differ from a typical experiment?
• 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, and the similarity of human sensory systems allows researchers to generalize from a small number of subjects
• 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. 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.
• Large N design


  Compares the performance of groups of subjects
• Small N design


  Studies one or two subjects, often using variations of the ABA reversal design
• Aggregate effects


  The pooled findings from many subjects
• 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
• Why a clinical psychologist might use small N designs

  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
• Why animal researchers prefer small N designs

  To minimize the acquisition and maintenance cost, training time, and possible sacrifice of their animal subjects
• Historical development that 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
• Where small N designs have been most extensively used

  Operant conditioning research, where B. F. Skinner examined the continuous behavior of individual subjects in preference to analyzing discrete measurements from separate groups of subjects
• 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
• Why many clinical researchers 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
• When is this most important

  When relapse threatens the health or safety of the patient or others, as in self-injurious, suicidal or homicidal behavior
• Price 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
• Multiple baseline design


  A series of baselines and treatments are compared within the same subject, and once treatments are administered, they are not withdrawn
• How multiple baseline design overcomes the ethical problem of withdrawing an effective treatment

  An experimenter never withdraws treatments after administering them
• How researchers analyze data from small N experiments

  Researchers often visually inspect changes in the dependent variable across treatment conditions, and may also use statistics to analyze small N data