Chapter 14: Small-N Designs

Question 1

Small-N designs

Answer 1

True experiments
Manipulation of one/more IVs and comparison of outcome on the DV
Involve observing an individual before and after treatment

Question 2

Methods of dealing with unstable data (3)

Answer 2

1) Wait until data stabilizes
2) Average a set of 2/more observations
3) Look for patterns within the inconsistencies

Question 3

Interrupted time series designs

Answer 3

Ongoing flow of events/time is interrupted by the introduction of a treatment at some specific point in time
Repeated measurements of the same participants

Question 4

Types of interrupted time series designs (4)

Answer 4

1) AB designs
2) Stable baseline designs
3) Multiple measures designs
4) ABA & ABAB reversal designs

Question 5

AB design

Answer 5

A single before and after measurement is taken

Question 6

Stable baseline design

Answer 6

Multiple pre-treatment measures are taken to ensure that the baseline is stable and there is no evidence of improvement before the treatment

Question 7

Multiple measures design

Answer 7

Multiple measures taken pre- and post-treatment to ensure that any changes are stable (reduces risk that difference is due to chance)

Question 8

ABAB design

Answer 8

Consists of 4 phases: baseline (A), treatment (B), withdrawal of treatment (return to baseline) (A), repetition of treatment phase (B)

Question 9

Problems with simple reversal design (2)

Answer 9

1) Irreversibility of treatment: not appropriate if long-lasting effects are expected
2) Ethics of discontinuing treatment: why remove an effective treatment?

Question 10

Types of multiple baseline designs (3)

Answer 10

1) Multiple-baseline across subjects
2) Multiple-baseline across behaviours
3) Multiple-baseline across situations

Question 11

Multiple-baseline across subjects

Answer 11

Same treatment, different participants

Question 12

Multiple-baseline across behaviours

Answer 12

Same treatment, different problems

Question 13

Multiple-baseline across situations

Answer 13

Same treatment, different situations

Question 14

Advantages of small-N designs (3)

Answer 14

1) Can establish cause and effect with single participant
2) Integrates experimental research into applied clinical settings
3) Flexibility

Question 15

Disadvantages of small-N designs (3)

Answer 15

1) Low external validity
2) Multiple observations required (subject reactivity?)
3) No statistical control