Single subject experimental design

SINGLE SUBJECT
EXPERIMENTAL DESIGN
Mohsen Sarhady MSc. OT & Soraya Gharebaghy MSc. OT
© 2015 msarhady1980@gmail.com

©2015MSARHADY1980@GMAIL.COM
Outline
Introduction to SSED
Foundations for SSED
Common types of SSED
Data analysis in SSED
Evidence based practice and SSED
Evaluating quality of SSED
Ethical issues in SSED

Think of these research problems:
 If there were two hearing aids, which one is more suitable for a 5
years old child with cerebral palsy and inability in lip-reading?
 Is repeated storybook reading with adult scaffolding effective in
increasing spontaneous speech in a 3 years old autistic boy?
 Which one is more effective in enhancing the ability to stand up
from chair in a child with dyskinetic CP: A weighted vest or
strengthening exercise?

INTRODUCTION
What is Single Subject Experimental Design

What is SSED?
 A research methodology in which the subject serves as
his/her own control, rather than using another
individual/group
 Single-subject (sometimes referred to as single-case or
single-system) designs offer an alternative to group designs
 The focus is on an N=1, a single subject, in which the “1” can
be an individual or a group of individuals

The unit of clinical interest: the individual
Example:
Gait training Results for 30 subjects:
Improved: 10
Unchanged: 10
Decline: 10
Conclusion: treatment had no effect

Assumptions of the SSED

Contrast between Case Study and SSED
 The case study is a subjective description of an individual's
behavior
 Providing impetus for further study or for the generation of theoretical
hypotheses
 Case study lacks variable controls and systematic data
collection
 It cannot document causal relationships between intervention and
changes in behavior
 Single subject research demands
 careful control of variables,
 clearly delineated and reliable data collection,
 and the introduction and manipulation of only one intervention
at a time

Contrast between Group Designs and SSED
 Group designs do not naturally conform to practice
 Particularly when the practice involves interventions with
individuals
 Analysis of group designs typically refers to:
 “group’s average change score” or
 “the number of subjects altering their status.”
 In group design we miss each individual’s experience
with the intervention
 Individual participants within the group may not respond to the
particular type of treatment offered

FOUNDATIONS
For Single Subject Experimental Design

Components of SSED
 The underlying principle of a single subject design:
 If an intervention with a client or a group of individuals is
effective, it should be possible to see a change in status
from the period prior to intervention to the period during
and after the intervention.
 This type of design minimally has three components:
 (a)Repeated measurement,
 (b)Baseline phase, and
 (c)Treatment phase

Repeated Measurement
 Single-subject designs require the repeated measurement
of a dependent variable (target problem)
 Prior to starting and during the intervention, we must be
able to measure the subject’s status on the target problem
at regular time intervals, whether the intervals are hours,
days, weeks, or months

Baseline Phase
 The period in which the intervention to be evaluated is not
offered to the subject
 Abbreviated by the letter “A”
 During the baseline phase, repeated measurements of the
dependent variable are taken
 These measures reflect the status of the client(s) on the
dependent variable prior to the implementation of the
intervention
 Baseline measurements provide two aspects of control
analogous to a control group in a group design:
 Its scores serve as a control group
 Its repeated measures solve the internal validity issue

Treatment Phase
 The time period during which the intervention is implemented
 Signified by the letter “B”
 During the treatment phase, repeated measurements of the
same dependent variable using the same measures are
obtained
 Patterns and magnitude of the data points are
compared to the data points in the baseline phase to
determine whether a change has occurred
 It is recommended that the length of the treatment phase be
as long as the baseline phase

Measuring Dependent Variable
 Measures of behaviors, status, or functioning are often
characterized in four ways:
 Frequency refers to counting the number of times an event occurs
 Duration refers to the length of time an event or some symptom
lasts and usually is measured for each occurrence of the event or
symptom
 Interval refers to length of time between events
 Magnitude refers to intensity of a particular event, behavior or
state
 The measures of phases are almost always summarized on a graph
 The y axis: used to represent the scores of the dependent
variable,
 The x axis: represents a unit of time, such as an hour, a day,
a week, or a month

COMMON TYPES
Of Single Subject Experimental Design

Common types of SSEDs
 Basic Design (A-B)
 Withdrawal Designs (A-B-A)
 Multiple Treatment Designs
 Multiple Baseline Designs
“A” : No treatment phases
“B”: Treatment phases

Basic Design (A-B)
 An A-B design represents a baseline phase followed by a treatment
phase
 No causal statements can be made
 A-B design provides evidence of an association between the
intervention and the change

Withdrawal Designs
 There are two withdrawal designs:
 the A-B-A design
 the A-B-A-B design
 Withdrawal:
 Intervention is concluded (A-B-A design) or
 is stopped for some period of time before it is begun again (A-
B-A-B design)
 The premise:
 If the intervention is effective, the target problem should be
improved only during the course of intervention
 The target scores should worsen when the intervention is
removed

A-B-A Design:
 Based on the A-B design by integrating a post-treatment follow-up
 This design answers the question left unanswered by the A-B design:
 Does the effect of the intervention persist beyond the period in which
treatment is provided?
 It may also be possible to learn how long the effect of the intervention
persists
 The follow-up period should include multiple measures until a follow-
up pattern emerges
 A-B-A design provides additional support for the effectiveness of an
intervention

A-B-A-B Design:
 The A-B-A-B design builds in a second intervention phase
 The intervention is identical to the intervention in the first B
phase

Multiple Treatment Designs
 The nature of the intervention changes over time, and each
change represents a new phase of the design
 One type of change that might occur is the intensity of the
intervention: (A-B1-B2-B3)
 Another type of changing intensity design is when you add
additional tasks to be accomplished (e.g. The B1 may involve
walking safely within the house, the B2 may add methods for
using a checkbook, the B3 adds a component on cooking)
 Other type: the actual intervention may change over time (A-B-C-
D)

Limitation:
 Only adjacent phases can be compared so that the effect for
nonadjacent phases cannot be determined
 There might have been a carryover effect from the previous
interventions

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

 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

Multiple base
design across
behavior
There is one
client, and the
same intervention
is applied to
different but
related problems
or behaviors

 Multiple base
design across
subjects
 each subject receives
the same intervention
sequentially to
address the same
target problem

 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

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

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

Internal validity
 How confident that changes in the
dependent variable are due to
introduction of independent variable
and not to some other factors.

Threats of internal validity
 Confound variable
 Maturation effects
 History effects
 Statistical regression toward
the mean

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

External validity
 Whether their finding applicable
to subjects and or settings
beyond the research.

Data Analysis in
Single-Case
research

 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.

 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?

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

Visual Analysis
 Differences in level
 Changes in trend or slope:
direction of the trend/ rate of increase or
decrease
 Change in variability

A) simple method to describe the level is to
inspect the actual data points
Differences in level

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).

Changes in level are typically used when the
observations fall along relatively stable
lines.

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

Nugent Method

Split-middle lines

Variability
 stability or variability of the data
points
draw range lines

Interpreting Visual Patterns
 patterns of level and trend

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

trend changes

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.

No change in…..? Change in…..?

No change in….?
Change in…..?

No Change in……?
Change in……?

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

PND≥90% Very effective treatment
PND 70-90 % Effective treatment
PND 50-70 % Questionable effectiveness
PND< 50 Ineffective treatment

conservative dual-criterion(CDC)
 mean line based on baseline
data
 split-middle line is calculated
based on baseline data

Question??

EVIDENCE BASED PRACTICE
And Single Subject Experimental Design

Evidence based medicine (that is, EBP specific to the field
of medicine) is “the conscientious, explicit, and judicious
use of current best evidence in making decisions about the
care of individual patients . . . [by] integrating individual
clinical expertise with the best available external clinical
evidence from systematic research”

EVALUATING QUALITY
Of Single Subject Experimental Design

Questions for evaluating quality of SSED
DESCRIPTION OF PARTICIPANTS AND SETTINGS
1. Was/were the participant(s) sufficiently well
described to allow comparison with other studies or
with the reader’s own patient population?

INDEPENDENT VARIABLE
2. Were the independent variables operationally
defined to allow replication?
3. Were intervention conditions operationally defined
to allow replication?

DEPENDENT VARIABLE
4. Were the dependent variables operationally defined as
dependent measures?
5. Was interrater or intra-rater reliability of the dependent
measures assessed before and during each phase of the study?
6. Was the outcome assessor unaware of the phase of the
study (intervention vs control) in which the participant was
involved?
7. Was stability of the data demonstrated in baseline, namely
lack of variability or a trend opposite to the direction one
would expect after application of the intervention?

DESIGN
8. Was the type of SSED clearly and correctly stated, for
example A–B, multiple baseline across subjects?
9. Were there an adequate number of data points in each
phase (minimum of five) for each participant?
10. Were the effects of the intervention replicated across
three or more subjects?

ANALYSIS
11. Did the authors conduct and report appropriate visual
analysis, for example, level, trend, and variability?
12. Did the graphs used for visual analysis follow standard
conventions, for example x- and y-axes labeled clearly and
logically, phases clearly labeled (A, B, etc.) and delineated
with vertical lines, data paths separated between phases,
consistency of scales?
13. Did the authors report tests of statistical analysis, for
example celeration line approach, two-standard deviation
band method, C-statistic, or other?
14. Were all criteria met for the statistical analyses used?

External validity of SSED
• Three sequential replication strategies to enhance the
external validity:
Direct replication:
repeating the same procedures, by the same researchers,
including the same treatment, in the same setting, and in
the same situation, with different clients who have similar
characteristics
Systematic replication:
repeating the experiment in different settings, using different
providers, and other related behaviors
Clinical replication:
combining different interventions in the same setting and
with clients who have the same types of problems

ETHICAL ISSUES
In Single Subject Experimental Design

Like any form of research, single-subject designs require the
informed consent
Participants must understand that the onset of the intervention
is likely to be delayed until either a baseline pattern emerges or
some assigned time period elapses
The risks associated with prematurely ending treatment in
withdrawal may be hard to predict

Single subject experimental design

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