Chapter 13 - Simple N Experiments

Question 1

what does the N represent?

Answer 1

N = sample size

Question 2

what is the goal in simple N experiments?

Answer 2

to show that a phenomenon occurs reliably for an individual, not necessarily the average person (not looking to generalize)

Question 3

define small N experiments

Answer 3

• each participant is treated separately
• data for each individual is presented
• careful designs allow us to compare each person during treatment and control periods
• often used in therapeutic settings
• allows us to study unique populations and special cases (Phineas gage)

Question 4

what are the disadvantages to small N experiments?

Answer 4

• cannot have great external validity, but it often isn’t the main concern either

Question 5

define stable baseline desings

Answer 5

• record behaviour over a period of time to establish a baseline level
• then implement intervention and record same behaviour over time to see the change
• important to compare the treatment to natural behaviours
• trying to minimize maturation threat

Question 6

define multiple baseline designs

Answer 6

• stagger the introduction of intervention across variety of contexts, individuals or behaviours
• attempting to rule out history threat
• focus on coupling the intervention and observed change
• can also be the same behaviour across different particpants

Question 7

define reversal designs

Answer 7

• observe behaviuor, intervene and then take away the intervention
• fosuing on coupling the intervention and behaviour change, looking at specific linkage between the two
• can be unethical if the intervention works well for the person

Question 8

what validities go with small N designs?

Answer 8

internal: focus on design features to draw a causal conclusion
external: relation across participants, may not always be important if the goal is to improve a particular participant’s individual behaviour
construct: ensure measures are valid and reliable
statistical: often don’t use traditional statistics, focus more on quality of qualitative data