Lecture 18: Single-Subject Designs Flashcards
(44 cards)
1
Q
how do we statistically analyze between-subjects designs?
A
an independent subjects t-test or between-subjects ANOVA
2
Q
how do we statistically analyze within-subjects designs?
A
a dependent subjects t-test or repeated measures ANOVA
3
Q
Single-subject (single-case) designs
A
research designs that use the results from a single participant or subject
4
Q
goal of single-subject designs
A
Establish the existence of cause-and-effect relationships
5
Q
how are single-case experimental designs conducted
A
by manipulating an IV and controlling extraneous variables to prevent alternative explanations for the research results
6
Q
alternative explanations for research results in a single-subject design
A
- Baseline
- Repeated observations
- Replication
7
Q
evaluating results from a single-case study
A
- Data are evaluated in a simple graph because statistical tests for significance cannot be used
- Contrary to what is stated in the textbook, several statistical significance tests could be used to compare the level and trend between the A and B phases
- However, such a graph by itself is not enough to show that the treatment caused a behavioural change
8
Q
why are graphs not enough to show that the treament caused a behavioural change?
A
- No control over extraneous variables
- This could be the result of chance
9
Q
phase
A
series of observations of the same individual under the same conditions
10
Q
when are baseline observations made?
A
when no treatment is being administered
11
Q
baseline phase
A
a series of baseline observations
12
Q
baseline notation
A
Identified by the letter A
13
Q
treatment observations
A
observations made when a treatment is being administered
14
Q
treatment phase
A
a series of treatment observations
15
Q
treatment notation
A
- Identified by the letter B
- Subsequent treatments are identified by subsequent letters (ex. c, d, e)
16
Q
function of baseline and treatment annotation
A
Allows researchers to describe the phases in a study by using a sequence of letters
17
Q
consistent level
A
- A series of measurements that are all nearly the same magnitude
- Graphed data points cluster around a horizontal line
18
Q
consistent trend
A
- Differences from one measurement to the next that are consistently in the same direction and nearly the same magnitude
- Graphed data points cluster around a sloping line
19
Q
stability
A
- The degree to which the observations show a pattern of consistent level or consistent trend
- Stable data may show minor variations from a perfectly consistent pattern
- Variations should be relatively small and the linear pattern relatively clear
20
Q
strategies for dealing with unstable data
A
- Wait until the data stabilize
- Average a set of two or more observations
- Look for patterns within the inconsistency
21
Q
length of a phase
A
- To establish a pattern (level or trend) within a phase and to determine the stability of the data within a phase, a phase must consist of a minimum of three observations
- Two observations, by themselves, do not provide enough information to determine a pattern.
- Additional observations beyond the first two are essential to establish level, trend, and stability.
- Typically, five or six observations are necessary to determine a clear pattern
- When high variability exists in the data points, additional observations should be made
22
Q
phase change
A
a manipulation of the independent variable
23
Q
what are accomplished with phase changes?
A
- Implementing a new treatment
- Withdrawing a treatment
- Changing a treatment
24
Q
function of changing phases
A
Initiates a new phase in which the researcher collects observations under new conditions
25
when should we change phases?
When a clear pattern has emerged from the preceding phase
26
what factors should we consider when assessing participants' responses to phases?
- If they are improving without treatment, don’t implement the treatment.
- If they are deteriorating quickly, implement the treatment right away.
- If the treatment produces immediate and severe deterioration, cease or modify treatment immediately.
27
visual inspection techniques
characteristics that help determine whether there is a meaningful change between phases
28
examples of visual inspection techniques
- Change in the average level
- Immediate change in level
- Change in trend
- Latency of change
29
tools to enhance visual inspection techniques
- Use lines representing a level in each phase and a band representing +/- 2 standard deviations
- Use lines representing the trend in each phase
30
phases of an ABAB design
A baseline phase (A), followed by treatment (B), then a return to baseline (A), and finally a repetition of the treatment phase (B)
31
goal of an ABAB design
to demonstrate that the treatment causes changes in the participant’s behaviour
32
demonstrating that the ABAB design causes a change in behaviour
- The pattern of behaviour in each treatment phase (different from the pattern in each baseline phase)
- The changes in behaviour from baseline to treatment and from treatment to baseline (the same for each of the phase-change points in the experiment)
33
limitations of the ABAB design
- Withdrawal from treatment
- This may not result in a change in behaviour (the patient is cured)
- This may result in a slight change but not a return to the baseline
- This may cause an ethical problem if the treatment is working for the participant
34
variations on the ABAB design
- Sometimes researchers add a treatment or modify the sequence of baseline and treatment phases.
- Creates a complex design
- Researchers must incorporate new treatments into the phase sequence to prove a causal relationship.
- ABBCAC, ABCBC, etc.
35
multiple baseline design
- The treatment phase is initiated for one baseline
- Baseline observations continue for the other
- The treatment is initiated for the second baseline at a different time
36
multiple-baseline across subjects
the initial baseline phases correspond to the same behaviour for two separate participants
37
multiple-baseline across behaviours
the initial baseline phases correspond to two separate behaviours for the same participant
38
multiple-baseline across situations
the initial baseline phases correspond to the same behaviour in two separate situations
39
component analysis designs
- Each phase adds or subtracts one component of a complex treatment
- Determines how each component contributes to the overall treatment effectiveness
- Component analysis with a reversal design
- Component analysis with a multiple-baseline design
40
rationale for multiple-baseline designs
- The criteria for a successful multiple-baseline experiment are essentially identical to the criteria described earlier to define the success of an ABAB design
- There is a clear and immediate change in the pattern of behaviour when the researcher switches from a baseline to a treatment phase
- The design includes at least two demonstrations that behaviour changes when the treatment is introduced
- This replication is necessary to establish a causal relationship between treatment and behaviour
41
strength of a multiple-baseline design
- No return to baseline needed
- Good for long-lasting treatments
42
weaknesses of a multiple-baseline design
- It can be difficult to identify similar but independent behaviours
- Results can be compromised by individual differences between participants or between behaviours
43
strengths of single-case designs
- Allows researchers to establish cause-and-effect relationships with one participant or subject
- Flexibility: the researcher is free to modify the treatment or change to a new treatment if a participant or subject fails to respond to the treatment
- No need to standardize treatment across groups—a single participant or subject is used
44
weakenesses of single-case designs
- Relationship among variables is for only one participant or subject
- May threaten external validity (generalization)
- Multiple, continuous observations are required
- Absence of statistical controls
- Reliance on graphs to display data
- Treatment effects must be large and immediate to produce a convincing graph
