Single subject experimental design (SSED) is a research methodology that focuses on measuring the effect of an intervention on a single subject or small group of subjects. It involves repeated measurement of a dependent variable during a baseline phase without intervention and a treatment phase with the introduction of an intervention. Common SSED types include basic A-B designs, withdrawal A-B-A designs, multiple treatment designs, and multiple baseline designs. Data is typically graphed and analyzed visually to determine if changes in level, trend, or variability correspond to the introduction of an intervention. SSED allows for testing interventions on individuals and can provide evidence of the effectiveness of clinical treatments.
27. Multiple Baseline Designs
A single transition from baseline to treatment
(AB) is instituted at different times across
multiple clients, behavior or settings.
This staggered or unequal baseline period is
what gives the design its name.
Internal validity is ensured by the multiple
replications of the intervention delivered
across client, behaviors or settings.
27single subject design
29. Each transition from baseline to
intervention is a opportunity to observe the
effects of treatment.
Transition at different times allows to rule
out alternative explanations for behavior
change
Concurrent measurement controls better
for threats to internal validity
29single subject design
31. Multiple base
design across
subjects
each subject receives
the same intervention
sequentially to
address the same
target problem
31single subject design
32. Multiple
base design
across
settings
Multiple baseline
designs can be
applied to test the
effect of an
intervention as it is
applied to one client,
dealing with one
behavior but
sequentially applied
as the client moves
to different settings
32single subject design
33. Multiple-baseline designs
strengths
internal validity
no reversal or withdrawal of
the intervention
they are useful when behaviors
are not likely to be reversible
Weakness: require more data
collection time
33single subject design
34. Eliminating Alternative
Hypotheses
By systematically delivering the
treatment and continuously
measuring the relevant target
behavior, change in the behavior
can be monitored and conclusions
drawn about determinates of this
change.
Ability to eliminate alternative
explanations for behavior change
34single subject design
35. Internal validity
How confident that changes in the
dependent variable are due to
introduction of independent variable
and not to some other factors.
35single subject design
36. Threats of internal validity
Confound variable
Maturation effects
History effects
Statistical regression toward
the mean
36single subject design
37. Threats to internal validity are
controlled primarily through:
Replication: each replication allows for a
comparison between the subject behavior during
baseline and during treatment:
a) Phase change
b) Intersubject replication
Repeated measure
37single subject design
38. External validity
Whether their finding applicable
to subjects and or settings
beyond the research.
38single subject design
40. Isolate causal relationships between
independent and dependent variables
By systematically delivering the
treatment (independent variable) and
continuously measuring the relevant
target behavior (dependent variable),
changes in behavior can be monitored.
40single subject design
41. single-subject researchers rely on visual
analysis of graphed data
Are there changes in the data patterns?
If changes do exist, do they correspond
with the experimental manipulations?
41single subject design
42. Data Graphs
Graphing the data facilitates monitoring and
evaluating the impact of the intervention
data for each variable for each participant or
system are graphed: dependent variable on the y-axis
& time (e.g., hour, a day, a week, or a month) on
the x-axis.
Graphing data for one variable for more than
one participant, the scale for each graph should
be the same to facilitate comparisons across
graphs
42single subject design
43. Visual Analysis
Differences in level
Changes in trend or slope:
direction of the trend/ rate of increase or
decrease
Change in variability
43single subject design
44. A) simple method to describe the level is to
inspect the actual data points
Differences in level
44single subject design
45. Differences in level
B) using the mean (the average of the
observations in the phase), or the median
(the value at which 50% of the scores in
the phase are higher and 50% are lower).
45single subject design
46. Changes in level are typically used when the
observations fall along relatively stable
lines.
46single subject design
47. Changes in Trend and slope
compare trends in the baseline and
intervention stages.
direction in the pattern of the data points
and can be increasing, decreasing,
cyclical, or curvilinear.
rate of increase or decrease
Magnitude and rapidity of behavior
transitions
Nugent’s method
split-middle lines
47single subject design
52. stable line (or a close approximation
of a stable line
A: the intervention has only made the problem worse,
B : the intervention has had no effect,
C: suggests that there has been an improvement
52single subject design
54. F: no effect
G:no change in the direction of the trend, but the rate of deterioration has
slowed
H: improved the situation only to the extent that it is not getting worse
I: improvement in the subject’s status.
54single subject design
58. The PND statistic
Percentage of nonoverlapping Data
Percentage of treatment data that
overlap with the most extreme data
point
Reduce maladaptive behavior: the
most extreme data point in baseline
with lowest numerical value
Increase adaptive behavior: most
extreme data point in baseline with
highest numerical value
58single subject design
زمانی که یک یا دو متغیر باعث شود میانگین تغییر کند استفاده از میانه روش مناسب تریست.
در این نمودار الگو و جهت شیب تغییر کرده اما میانگین همچنان ثابت است. که تعیین سطح روش مناسبی نیست.
When there is a trend in the baseline, you might ask
whether the intervention altered the direction of the trend. When the direction does not
change, you may be interested in whether the rate of increase or decrease in the trend has
changed. Does it alter the slope of the line?
Widely divergent scores in the baseline make the assessment
of the intervention more difficult, as do widely different scores in the intervention
phase. There are some conditions and concerns for which the lack of stability is the problem,
and so creating stability may represent a positive change. One way to summarize variability
with a visual analysis is to draw range lines,