Figuring out whether
that’s working for
your child
Single subject designs for families
Theodore A. Hoch, Ed.D., B.C.B.A.
George Mason University
Goals of today’s talk
 Help you to be an informed and discerning
consumer
 Give you ideas as to how to figure out whether the
therapy options you’ve chosen for your child are
working
Autism is diagnosed
when …
 At least six symptoms, including:
 At least two of these: A) nonverbal behavior impairment; B) relationships
impairment; C) lack of spontaneous interaction; D) lack of social reciprocity
 And, at least one of these: A) delay in onset or lack of speech; B) difficulty
initiating or sustaining a conversation; C) stereotyped or repetitive language; D)
lack of varied, imaginative, imitative play
 And, restricted, repetitive, and stereotyped interests, to include at least one of
these: A) stereotyped interests; B) inflexible routines and rituals; C) stereotyped
mannerisms; D) preoccupation with parts of objects
 And onset of delayed functioning prior to 3 years of age in at least one of
these: A) social interaction; B) conventional social communication; C)
symbolic or imaginative play
So,
 At least 7982 combinations of presentations that can
correctly be called “autism”
 This is where we get a spectrum!
 With 7982 different presentations:
 Is there an “average kid with autism?”
 Is your child “the average kid with autism?”
 Will what works for “the average kid with autism”
work with your child?
How to find what will work
for your child
 Look for empirically supported interventions
 Where?
 Internet
 POAC website and listserv
 Pediatrician / Neurologist
 Friends / neighbors / family
 TV / Radio
Empirically Supported
Interventions
 Catch-phrase for the 2000s?
 Backed by well-done research
 How to access it?
 Google, web pages
 Professionals
 People you trust
 Original sources
 GMU library, other libraries
Why is research done?
 To answer questions, such as:
 What happens if …
 Which of these is better …
Research is not done to …
 Find a predetermined solution
 Produce results that will support a particular
intervention
When should research be
done?
 Before a practitioner recommends or prescribes
a particular course of treatment for another
person
 Exception: the course of treatment is
experimental, and the person who is the
recipient of the treatment has given fully
informed consent
So I go to one of those places and
find some research. How do I know I
have an empirically supported
intervention?
 Validated by group design research
 Sufficient sample size
 Random sampling
 Random assignment
 Well-chosen, well-implemented experimental design
and controls
 Reliable measurement
 Well-chosen, well-conducted and interpreted
statistical analysis
 Replication in peer-reviewed journals
How do I know I have an empirically
supported intervention? (continued)
 Group design research
 Where to find?
 Outcome?
 Types of questions answered
 Which works better, on average?
 Which affects whom, on average?
 Who says / does / makes / chooses
what?
 Questions about groups
 Applicability to your child
 Feasibility for families?
Okay. So I go to a different source.
How do I know I have an empirically
supported intervention?

Validated by single subject design

Procedure described so you can do exactly what the
authors did

Authors describe where they did the study, so you can
compare their setting with yours.

Authors measure and report data on how well they actually
implemented their interventions.

Data and design indicates intervention (and not other
factors) changed the behavior

Findings replicated

At least 5 studies in peer-reviewed journals,

At least 3 different sets of researchers

At least 20 different participants.
How do I know I have an empirically
supported intervention?
 Single subject design
 Can involve one, a few, or many participants
 Focus always on individual participant
 Typically, no statistics
 Where to find?
 Outcome?
 Types of questions answered
 Which works better for this person?
 How does this work for this person?
 What happens if I do this, with this person?
 Questions about the individual and the intervention
 Applicability to your child
 Feasibility for families?
Controversial interventions
 Controversial = produces lively discussion and
disagreement
 Good? Bad?
 Work? Don’t work?
 In peer-reviewed journals? Less scholarly sources?
 Examples
 Can be effective, for some individuals (just like any
other intervention)
Types of interventions
 ABA / VB
 Auditory Integration
 Attachment / holding
 Typical special ed
 Dietary
 Specialized special ed
 Medication
 Occupational therapy
 Chelation
 Music therapy
 Vitamin / megavitamin
 Speech therapy
 Sensory Integration
 Hippotherapy
 Facilitated
Communication
 Aquatic therapy
 Others?
Empirically Supported v.
Controversial
 Any therapy can be one, the other, or both.
 Will an empirically supported intervention work for
your child?
 Will a controversial intervention work for your
child?
 Does it really matter, as long as it works?
 How do I know whether it’s working for my child?
 Go by my impressions
 Do the research
Using Research Designs to Evaluate
Your Child’s Intervention
 Group design
 Random selection
 Random assignment
 Sufficient number of
subjects
 Statistical Analysis
 Single subject design
 Work with who you have
 Interested in who you
have
 Your subject is her or his
own control
 Visual inspection of graphs
– no statistics analysis
(usually)
Using single subject designs to
evaluate your child’s
intervention
 Interested in effects on behavior of individuals – not
the average person!
 Your child provides
 Baseline data = data collected before you start /
in absence of the intervention
 Intervention data
 You compare your child’s baseline data with your
child’s intervention data
 Draw conclusions about how it works with your
child
Using single subject designs to
evaluate your child’s
intervention
 Participants and settings described thoroughly
 Were research participants similar to your child?
 Was procedure done in a setting similar to your
child’s?
 Were procedures implemented by people
similar to those who’ll implement your child’s?
 Were data collection procedures and
equipment similar to those you’ll use?
 Were professional supports used similar to those
you can access?
Using single subject designs to
evaluate your child’s
intervention
 Dependent variable – what you’re interested in
changing
 Usually, your child’s behavior
 Operationally defined
 What to do if professional opposes this?
Operationally defining
dependent variable
 Example:
 Intervention is semi-monthly IM injection and daily
subcutaneous injection of Lupron to decrease
symptoms of autism
 Which symptom(s)?
 Impulsivity
 What does person do that gets you to say he’s
behaving impulsively
 Hits, kicks, pinches, runs away when asked to do
something, destroys things, takes others’ things
 Dependent variable is these behaviors
Operationally defining
dependent variable
 Example:
 Intervention is brushing protocol to decrease
tactile defensiveness
 How do we know tactile defensiveness is
happening?
 Removes clothing when it is not time to do so
 Target is percentage day dressed, number of
times clothing removed per day, duration
dressed, etc.
Operationally defining
dependent variable
 Example:
 Intervention is a proton pump inhibitor taken to
decrease irritability, vomiting, and food refusal that
may be related to gastroesophageal reflux
disease
 How do we know irritability is happening?
 Crying, whining, complaining
 Target is crying, whining, complaining,
proportion of meal eaten, presence / absence
of vomiting
Operationally defining
dependent variable
 Example:
 Intervention is a differential reinforcement
procedure to improve compliance
 How do we know compliance is happening?
 Child initiates doing what parent or teacher
asked within 5 sec of parent or teacher asking
 Target is count / percentage of times child did
what was asked within 5 sec of having been
asked
Operationally defining
dependent variable
Describe exactly what you see and hear when
the thing you’re interested in is happening
Using single subject designs to
evaluate your child’s
intervention
 Dependent variable
 Repeated measurement
 Need at least three measures for a trend
 No conclusions based on a single measure
 Can differ, in medical situations
 Blood levels, range of motion measures,
videoesophagram observation, counts of
aggression, cold probes of correctly answering
“Wh” questions
How to Measure
 Choose a system you can do
 Well
 Regularly
 In the settings where it will need to be done
 By the people who’ll need to do it
 With the resources you have
 When you need to do it
 Reliably
How to Measure
 Direct measures
 Count – how many
 Duration – for how long
 Latency – started when, in relation to what
 Extensity – how big / over what area
 Rate – how fast / slow / many per ________
 Intensity – with how much force
 Interepisode time (interresponse time) – how much
time in between
How to measure
 Indirect measures – samples, estimates, or products of
phenomenon of interest
 Partial interval sampling
 Behavior too frequent to count, competing
responsibilities preclude counting
 Momentary time sampling
 Behavior too frequent to count, partial interval
sampling not feasible
 Permanent products
 Behavior produces distinct products, nothing else
produces those products
How to measure –
Partial interval sampling
 Decide on overall observation time
 Divide overall observation time into equal observation
intervals
 Observe
 Record
 The first time the dependent variable occurs during an
interval
 Whether or not the dependent variable occurred during
an interval
 Convert to percentage intervals of occurrence
How to measure –
Partial Interval Sampling
 Dependent variable = rumination
 16 hour observation time, 72 fifteen minute intervals
 Record R in cell if rumination was observed during
that time, N if no rumination was observed during that
time
 Convert to percentage intervals occurrence
How to Measure –
Partial Interval Sampling
How to measure – Momentary
Time Sampling
 Decide on overall observation time
 Divide overall observation time into equal observation
intervals
 Set countdown timer to signal end of interval
 Timer sounds  Observe  Record if dependent variable
is happening at that moment
 Do not record what is happening between times when the
timer sounds!
 Reset timer, continue
Partial Interval Sampling versus
Momentary Time Sampling
 Estimate
 Estimate
 Potentially more effortful,
more accurate than
momentary time
sampling
 Said to overestimate
 Potentially less effortful,
less accurate than
momentary time
sampling
 Use when purpose is to
decrease dependent
variable, more rigorous
procedures not feasible
 May underestimate
 Use when more
rigorous data
collection procedures
are not feasible
Converting
Partial
Interval
Sampling to
Scatterplots
How to Make a
Scatterplot
 Use Table feature in Word (or just draw a grid)
 Horizontal Axis – Day of the Week (or month)
 Vertical Axis – Time of Day (equal intervals)
 Color code dependent variables
 Shade in cell with relevant color when dependent
variable happens
 Examine for patterns of occurrence (and
nonoccurrence)
 Draw phase dividers, label conditions, when you
change conditions
 Continue data collection, inspection, and decision
making
Using single subject designs to
evaluate your child’s
intervention
Dependent variable
 Ensure reliability
Define clearly
Train data collectors
Conduct reliability observations
Take corrective / congratulatory action
based on reliability observations
Using single subject designs to
evaluate your child’s
intervention
 Independent variable – the intervention
 Describe clearly – put it in writing and refer to it
often!
 Train implementors until you see them actually
doing it correctly
 Check periodically to make sure it’s still being done
correctly
 Introduce at the right time
 Your data tell you when …
25
19
17
15
Day
23
Intervention
Baseline
21
Intervention
13
Baseline
11
16
14
12
10
8
6
4
2
0
Number of X by Day
9
Make them usable and
keep them handy!
Cum # Responses
4
11
14
15
17
1456
3813
6032
8391
11062
13746
16214
18138
18151
18153
18157
20135
22820
25318
28116
31010
33958
36333
36523
36525
36529
36532
38456
41134
43880
46558
49533
52545
52632
52637
52640
54685
57549
60342
60406
62898
62951
65699
65768
68236
68308
70989
71057
73924
7
Your data and your
graphs are tools!
# Responses
4
7
3
4
2
1439
2357
2219
2359
2671
2684
2468
1924
13
2
4
1978
2685
2498
2798
2894
2948
2375
190
2
4
3
1924
2678
2746
2678
2975
3012
87
5
3
2045
2864
2793
64
2492
53
2748
69
2468
72
2681
68
2867
5
Condition
Saline
Saline
Saline
Saline
Saline
Dzp
Dzp
Dzp
Dzp
Dzp
Dzp
Dzp
Sailine
Saline
Saline
Saline
Dzp
Dzp
Dzp
Dzp
Dzp
Dzp
Dzp
Saline
Saline
Saline
Saline
Dzp
Dzp
Dzp
Dzp
Dzp
Dzp
Saline
Saline
Saline
Dzp
Dzp
dzp
Saline
Dzp
saline
dzp
Saline
dzp
saline
Dzp
Saline
Dzp
3
Date
1.21.05
1.22.05
1.23.05
1.24.05
1.25.05
1.26.05
1.27.05
1.28.05
1.29.05
1.30.05
1.31.05
2.1.05
2.2.05
2.3.05
2.4.05
2.5.05
2.6.05
2.7.05
2.8.05
2.9.05
2.10.05
2.11.05
2.12.05
2.13.05
2.14.05
2.16.05
2.17.05
2.18.05
2.19.05
2.20.05
2.21.05
2.22.05
2.23.05
2.24.05
2.25.05
2.26.05
2.27.08
2.28.05
3.1.05
3.2.05
3.3.05
3.4.05
3.5.05
3.6.05
3.7.05
3.8.08
3.9.05
3.10.05
3.11.05
1
Session
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Number
Graphing your data – Why?
Graphing your data – How?
 Draw a grid (or use graph paper, or use Excel)
 Horizontal axis = days or sessions, with equal intervals
 Vertical axis = dependent variable measure, with
equal intervals
 Plot data point as soon as you get it
 Connect the dots (but not across phase dividers)
 Phase dividers and condition labels
Examine your data for:
Level
Treatment
Baseline
Number of Responses by Session
Baseline
Treatment
Baseline
Treatment
18
14
12
10
8
6
4
2
Session
29
27
25
23
21
19
17
15
13
11
9
7
5
3
0
1
Number of Responses
16
Examine your data for:
Trend
18
Number of Responses by Day
Treatment
Baseline
16
14
Number of Responses by Day
Treatment
Baseline
120
Number
12
10
8
6
4
2
100
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Day Number
Number
of Responses by Day
60
Treatment
Baseline
40
12
20
10
8
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Day
Number
Responses
0
80
6
4
2
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Day
Examine your data for:
Variability
Baseline
Number of Responses by Day
Treatment
Number of Responses
120
100
80
60
Less variability more stable
40
20
More variability Less stable
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Day
What’s happening –
Level, Trend, and Variability
Treatment
Baseline
Number of Responses by Session
Baseline
Treatment
Baseline
Treatment
18
14
12
10
8
6
4
2
Session
29
27
25
23
21
19
17
15
13
11
9
7
5
3
0
1
Number of Responses
16
What’s happening –
Level, Trend, and Variability
Number of Responses by Day
Number of Responses
30
Baseline
Treatment
Baseline
Treatment
25
20
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Day
What’s happening –
Level, Trend, and Variability
Number of Responses by Day
Baseline
Treatment
Baseline
Treatment
35
30
Number
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Day
What’s happening –
Level, Trend, and Variability
Number of Responses by Day
Baseline
Treatment
Baseline
Treatment
25
Number
20
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Day
Using a design
 Why?
 Demonstrating functional control
 Determining whether there’s been a change (and
how much)
 Determining whether the intervention produced
the change
 Determining how much of what you’re doing is
necessary
 Determining how much of what you’re doing is the
right amount
Did the intervention produce
the change? Three criteria
 Prediction
 If, by doing what I’m about to do, I’m
actually doing something different,
then I should see a different pattern
from my dependent variable.
 If, by doing what I’m about to do, I’m
really not doing anything different from
what I was already doing, then I should
see the same pattern from my
dependent variable
Prediction
 Prediction criterion not
satisfied
 Prediction criterion
satisfied
Number of X by Day
Baseline
Intervention
14
14
12
12
10
8
6
8
6
4
2
2
0
0
2
3
4
5
6
Day
7
8
9
10
11
Intervention
10
4
1
Baseline
16
Number
Number
16
Number of X by Day
1
2
3
4
5
6
Day
7
8
9
10
11
Did the intervention produce
the change? Verification
 If the change that came about is attributable to the
intervention, then when I stop doing the intervention,
my dependent variable should return to (or trend
toward) its state.
 If the change that came about is attributable to
something else, then when I stop doing the
intervention, the dependent variable should
continue as though nothing had changed.
Prediction and Verification
 Predication and
Verification Satisfied
 Prediction Satisfied,
Verification Not
Satisfied
Number of X by Day
Baseline
16
Intervention
Number of X by Day
Baseline
14
Number
Number
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Day
Intervention
Baseline
18
16
14
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
Baseline
9 10 11 12 13 14 15 16 17 18
Day
Did the intervention produce
the change? Replication
 I did my intervention, my dependent variable measure
changed.
 I stopped doing the intervention, and my dependent
variable returned to its baseline state.
 I’m doing the intervention again.
 If the dependent variable changes as it did before,
the intervention produced the change.
 If the dependent variable measure doesn’t change,
then something else produced the change before.
Prediction, Verification, and Replication
 Prediction, Verification,
and Replication Criteria
Satisfied
 Prediction and
Verification Criteria
Satisfied, Replication
Criterion not Satisfied
25
23
19
17
15
Day
21
Intervention
Baseline
13
11
9
Intervention
7
Baseline
5
25
23
21
19
17
13
11
9
7
5
15
Day
16
14
12
10
8
6
4
2
0
3
Intervention
Baseline
1
Intervention
Number of X by Day
Number
Baseline
3
16
14
12
10
8
6
4
2
0
1
Number
Number of X by Day
AB Design
A = Baseline
B = Intervention – introduce after you have:
 Stable baseline (no trend)
 Baseline data trending in direction opposite to that
which the intervention is intended to move the
data
 Repeating variability in the data
So, if goal is to increase behavior, when
should I introduce the intervention?
Dependent Variable Measure by Day
Dependent Variable Measure by Day
40
50
A
Dependent Variable Measure
Dependent Variable Measure
50
30
20
10
40
B
30
20
10
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Day
Day
Dependent Variable Measure by Day
Dependent Variable Measure by Day
40
50
C
Dependent Variable Measure
Dependent Variable Measure
50
30
20
10
0
40
D
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Day
Day
If goal is to decrease the behavior, when
should I introduce the intervention?
Dependent Variable Measure by Day
Dependent Variable Measure by Day
40
50
A
Dependent Variable Measure
Dependent Variable Measure
50
30
20
10
40
B
30
20
10
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Day
Day
Dependent Variable Measure by Day
Dependent Variable Measure by Day
50
40
C
Dependent Variable Measure
Dependent Variable Measure
50
30
20
10
0
40
D
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Day
Day
Introduce intervention (or
change conditions) when:
 Data are stable (e.g., flat trend, minimal variability), or
 Data are trending in direction opposite to that which
the next condition would be intended to move it, or
 Variability is reliably repeating
AB Design
•Prediction?
•Verification?
•Replication?
•Functional relation?
•Questions answered
•Controlling for
potential confounding
variables
•Good enough?
AB Design
 Prediction?
 Verification?
 Replication?
 Functional relation?
 Questions answered
 Controlling for
potential
confounding
variables
 Good enough?
When might you want to use an
AB Design?
(Behavioral and not behavioral
examples)
ABA Design
 A = Baseline
 B = Intervention
 Same rules for introducing intervention or
changing conditions apply
ABA Design
 Prediction?
 Verification?
 Replication?
 Functional relation?
 Questions answered
 Controlling for
potential confounding
variables
 Good enough?
 Ethical or other
concerns?
When might you want to use
an ABA Design?
BAB Design
 A = Baseline
 B = Intervention
 Intervention implemented without getting baseline
data first as the person’s current condition is
sufficiently bad as to render taking baseline data
undesirable
 Same rules for introducing intervention or changing
conditions apply
BAB Design
Percentage Bites and Drinks Accepted by Meal
Intervention
Baseline
Intervention
100
90
80
Percentage
70
60
50
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Meal
•Prediction?
•Verification?
•Replication?
•Functional relation?
•Questions answered
•Controlling for
potential confounding
variables
•Good enough?
When might you want to use a
BAB design? (Behavioral and not
behavioral examples)
ABAB (Withdrawal)
Design
 A = Baseline
 B = Intervention
 Same rules for introducing intervention or
changing conditions apply
ABAB (Withdrawal)
Design
•Prediction?
•Verification?
•Replication?
•Functional relation?
•Questions
answered
•Controlling for
potential
confounding
variables
ABAB (Withdrawal)
Design
•Prediction?
•Verification?
•Replication?
•Functional relation?
•Questions
answered
•Controlling for
potential
confounding
variables
ABAB (Withdrawal)
Design
•Prediction?
•Verification?
•Replication?
•Functional relation?
•Questions answered
•Controlling for potential
confounding variables
ABAB (Withdrawal)
Design
•Prediction
•Verification
•Replication
•Functional
relation
between
intervention and
dependent
variable?
ABAB (Withdrawal)
Design
•Prediction
•Verification
•Replication
•Functional
relation
between
intervention and
dependent
variable?
When might you use an ABAB
Design?
(Behavioral and not
behavioral examples)
Multiple Treatment
Withdrawal Design
 A = Baseline
 B = Treatment 1
 C = Treatment 2
 D = Treatment 3, etc.
 Same rules for introducing intervention or
changing conditions apply
Multiple Treatment Withdrawal
Design
•Prediction
•Verification
•Replication
•Functional relation
between intervention
and dependent
variable?
•Be aware of possibility
of sequence effects (or
multiple treatment
interference effects)
When might you want to use a
multiple treatment withdrawal
design?
(Combined medication and
behavioral treatment, possibly.
Others?
Component Analysis with a
withdrawal design
 A = Baseline
 B = a treatment package, made up of multiple
components
 After stable data pattern attained during treatment
package, remove one or more components at a time,
with whole package implemented in between,
comparing level of dependent variable between
conditions
 Determines which of the treatment components are
necessary and sufficient to maintain the improvements
(and which you can drop)
Component Analysis with a
withdrawal
design
•Prediction
•Verification
Dependent Variable Measure by Day
BL
60
•Functional relation?
50
•How do you know
what to keep and
what to drop?
Dependent Variable Measure
•Replication
1, 2, & 3
1& 2 1,2,&3
1 & 3 1,2,&3
2 & 3 1, 2, & 3
1& 2
40
30
20
10
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Day
When might you want to use a
component analysis?
Considerations with Withdrawal
Designs
 Reactivity between baseline procedure and
dependent variable
 History effects
 Sequence effects
 Irreversibility of some behaviors (e.g., bicycle
riding, reading, behaviors coming under control
of “natural contingencies”)
 Failure to return to baseline level
 Ethical considerations
 “Bad idea” behaviors
 “He’s doing so well, why withdraw treatment?”
Multiple Baseline
Designs
 A = Baseline
 B = Intervention
 Good when you need to work on
 One dependent variable, but in multiple settings
 One dependent variable, but with multiple
“therapists”
 More than one dependent variable
 And you can’t get to it all at once!
Multiple Baseline Design
Across Settings
Baseline
Percentage Meals Consumed by Day
Intervention
100
90
•Prediction
70
Percentage
•Verification
80
60
50
40
30
20
•Replication
10
0
100
•Functional relation?
90
80
Percentage
70
60
50
40
30
20
10
0
100
90
80
Percentage
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
Day
12
13
14
15
16
17
18
19
20
21
Multiple Baseline Design
Across Academic Behaviors
•Prediction
•Verification
•Replication
•Functional relation?
Multiple Baseline Design
Across Academic Behaviors
•Prediction
•Verification
•Replication
•Functional relation?
Stacked Graphs do not
make a Multiple Baseline
Design!
Stacked Graphs with a Squiggly Line
Don’t Make A Multiple Baseline
Design!
When might you want to use a
Multiple Baseline Design?
What works (or will work) for
your child?
 Up to you to decide.
 Empirically supported versus
controversial
 Sometimes a false dichotomy
 Possibly irrelevant
What works (or will work)
for your child?
• Be an informed consumer
•Magazines versus journals
•Second hand versus original reports
•The speaker or author versus the procedure and
the data
•Skeptical consideration of the design, data, and
conclusions drawn from them
•Understand that selecting an empirically
supported intervention does not obviate need to
evaluate the intervention with your child!
Single subject research designs
are tools to help decide.
 Single subject research designs are tools to help
decide whether or not what you’re doing is helping
 The data are right, and the child is right
 Impressions are often inaccurate
 Designs provide a way to more objectively examine
how things are going, and with which to make
differently informed decisions for our children