Foundations of
Research
15. Within-subjects & blocking designs
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© Dr. David J. McKirnan, 2014
The University of Illinois Chicago
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1
Foundations of
Research
Within-subjects designs.
 Previous modules discussed
experimental approaches:
 Participants are assigned to different groups;
 The unit of analysis is the difference
between groups.
 …and correlational designs:
 Participants have scores on each of two or
more variables,
 We assess the strength of association
between variables, across participants.
 Here we will review within-subject
designs:
 Participants have scores for each of two or
more experimental conditions.
2
Foundations of
Research
Basic forms of within-subjects designs, 1
Basic forms of within subjects designs;
1. Own control

Each participant in control and experimental group.

Optimally, order is counter-balanced
2. Reversal designs
3. Repeated measures & Randomized block designs
3
Foundations of
Research
Basic forms of within-subjects designs, 3
Basic forms of Within subjects designs;
1. Own control
2. Reversal designs

Hypothesis: behavior controlled by clearly bounded
condition

Design: “A – B – A”; impose – withdraw – impose
condition
3. Repeated measures & Randomized block
designs
4
Foundations of
Research
Basic forms of within-subjects designs, 2
Basic forms of Within subjects designs;
1. Own control
2. Reversal designs
3. Repeated measures

Multiple treatment conditions: each participant gets
each treatment.

Longitudinal / time sampling: each participant
assessed over multiple time periods

Randomized block designs; Repeated measure
combined with between-groups variable.
5
Foundations of
Research
Within-subjects & blocking designs
 Own Control designs
Reversal designs
Repeated measures & Randomized block
designs
6
Foundations of
Research
Your personal own-control design:
The McGurk effect
Click the image for a video demonstrating a
powerful audio-visual illusion.
Click the box to begin.
Imagine you are watching these stimuli as if
you are a participant in an own-control
research design…
YouTube: jtsfidRq2tw
7
Foundations of
Research
This experiment

The video presents the syllable “ga”

The audio presents “ba”

Did the illusion work for
The brain “fuses” them so most
people perceive “da”.
you?

This is an audio-visual illusion.

The within-subjects design allows us to clearly
contrast these stimuli within the same people;

Your own experience of the stimuli change across condition.

You are in both the control group and the experimental
group.
8
Basic experiments: “BetweenSubjects”
designs
Group 1
Experimental condition /Treatment
Observe
Foundations of
Research
Between-subjects designs
9
1
Group 2
2 separate groups:
Observed (naturally
occurring) or
Randomly Assigned
to be equivalent
Control condition
Observe2
Independent Variable:
Dependent Variable(s):
One group receives the
experimental condition
or treatment, one does
not.
Measured in both groups.
 Clear experimental manipulation; treatment given to only
one group.
 Hypothesis tested by differences between groups.
 Internal validity: groups must differ only on Independent
variable; non-equivalence at baseline = confound
 Statistical power requires large number of subjects.
Why do “Within – Subjects” designs?
Foundations of
Research



Within Ss designs
Many research questions address contrasts between
different states within one person

Alcohol v. non-alcohol use  aggression, risk

Learning condition v. recall state  “State dependent learning”
Many studies address change over time

Behavioral or biomedical intervention studies

“Natural history” or cohort studies
Practical efficiency of within Ss designs

Powerful contrasts within a participant; less within-group
(error) variance.

Studies that require rare or costly participants.
10
Foundations of
Research
11
Pre- Post- designs
Non-experimental within-subjects approach
Single
Group
Baseline
(Observe1)
All participants are assessed at
baseline
Single
Group
Assess
smoking
rate
Experimental
Manipulation
Follow-up
(Observe2)
All participants then get the
Experimental intervention and
follow-up measurement.
10-week
smoking
cessation
program
Follow-up
smoking
rate
Foundations of
Research
Single
Group
12
Pre- Post- designs
Baseline
(Observe1)
All participants are assessed at
baseline
Experimental
Manipulation
Follow-up
(Observe2)
All participants then get the
Experimental intervention and
follow-up measurement.
 Hypothesis is tested by change from baseline
 This is a main effect analysis
 “Groups” = baseline v. follow-up scores
 Possible confounds?
 History
 Maturation
 Regression…
Foundations of
Research
13
“Own Control”
True experiment with within-subjects design
Single
Group
Control
Condition
Observe1
All participants get the Control
condition and measurement
Single
Group
Learning
under quiet
conditions
Recall
task
Experimental
Condition
Observe2
All participants then get the
Experimental intervention and
measurement.
Learning
under ‘white
noise’
condition
Recall
task
Foundations of
Research
Single
Group
14
“Own Control”
Control
Condition
Observe1
Experimental
Condition
Observe2
 Each participant is his own “control group”
 Hypothesis tested by differences between conditions
(Observation1 v. Observation2) within group.
 Internal validity: eliminate possible confound of group
differences at baseline, since there is only one group.
 Experimental manipulation potentially less clear due to
possible carry-over effects.
 Statistical power increased: requires fewer subjects.
Own Control design with counter-balancing
Foundations of
Research
Group 1
Control
Condition
Observe1
Experimental
Condition
Observe2
Group 2
Experimental
Condition
Observe1
Control
Condition
Observe2
2 groups, naturally
occurring or
randomly assigned
Basic own-control design done twice.
Group 1
Learning
under quiet
conditions
Recall
task
Learning
under ‘white
noise’
condition
Recall
task
Group 2
Learning
under ‘white
noise’
condition
Recall
task
Learning
under quiet
conditions
Recall
task
15
Foundations of
Research
16
Own Control design with counter-balancing
Group 1
Control
Condition
Observe1
Experimental
Condition
Observe2
Group 2
Experimental
Condition
Observe1
Control
Condition
Observe2
Own-control design done twice.
 Test the Hypothesis: by combining the 2 groups.
 Internal validity:
 eliminate confound of group differences at baseline.
 Lessens (& allows for test of) carry-over effects
 Statistical power design requires more subjects.
Survey example of own-control design
Foundations of
Research
Phenomenon:
Treatments for HIV lower the amount of virus in the blood
(“viral load”), which may make the person less infectious.
Hypotheses:
1. Gay men decide how risky an HIV-infected partner
is based on whether the partner has a low viral
load.
2. Men who are particularly risk-prone are more likely
to decide someone is “safe” via viral load
information.
Vanable, P.V., Ostrow, D.G., McKirnan, D.J., Tayawaditep, J., & Hope, B.A. (2000). Impact of combination therapies on HIV risk
perceptions and sexual risk among HIV-positive and HIV-negative gay and bisexual men. Health Psychology,19(2), 1-12.
17
Foundations of
Research
Design: Community survey of Chicago gay men
18
IV# 1; Repeated measure: hypothetical partner
– The participant reads about each of 2 potential partners:
An HIV+ man who is not in treatment
An HIV+ men in treatment, with a low viral load
– Then rates how risky each partner might be.
True independent variable; manipulated by the experimenter.
IV #2; Blocking variable: participants’ risk status
– Participants describe their history of sexual risk and are
categorized by the experimenter as ‘high’ v. ‘low’ risk
Measured “blocking” variable only.
Foundations of
Research
19
Mixed Repeated Measures: data structure
Repeated measure:
All participants respond to both
conditions.
Blocking
variable:
Men “assigned”
to high v. low
risk groups
based on their
interview
answers.
Sexual partner scenario
Participant
risk
background
HIV+ man who is in
treatment, with a low
viral load
HIV+ man who is
not in treatment
Low risk
2, 3, 11, 5, 9, 12,
13, 16… n = 488
2, 3, 11, 5, 9, 12,
13, 16… n = 488
High risk
1, 4, 6, 7, 8, 10,
14, 15… n = 66
1, 4, 6, 7, 8, 10,
14, 15… n = 66
Subject #
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Risk level
H
L
L
H
L
H
H
H
L
H
L
L
L
H
H
L
Foundations of
Research
Survey: repeated & blocked analyses, 4
20
Data:
A statistical interaction between the
repeated measure and the blocking variable.
Vanable, P.V., Ostrow, D.G., McKirnan, D.J., Tayawaditep, J., & Hope, B.A. (2000). Impact of combination therapies on HIV
risk perceptions and sexual risk among HIV-positive and HIV-negative gay and bisexual men. Health Psychology,19(2), 1-12.
Foundations of
Research
Survey: repeated & blocked analyses, 5
Data: Both risk groups
consider an HIV+ partner
with low viral load to be
safer
“Low” viral load
information lowers
risk perception most
for the risky men.
Giving all participants both
conditions creates a strong
contrast (Independent Var.).
21
Foundations of
Research



Advantages of own-control designs
22
Participant assignment problems eliminated, since
there is only one group.

No need for random assignment or matching procedures

No possible problem with groups differing at baseline.
Can use a smaller n than between S’s

Half as many participants for simple own-control
experiment

Very useful for rare or expensive participants
More sensitive to experimental effects; less withinsubject (error) variance
Foundations of
Research

Sequencing or order effects:

Scores change if participants learn or get practice over
different conditions

Participants get sensitized to experiment or
experimental procedures

Cure by counter-balancing order of condition.


Disadvantages of own-control designs
Takes as many participants as between - group design
Participant “burden”

Fatigue from multiple experimental tasks

Possible drop-out
23
Foundations of
Research
Within-subjects & blocking designs
Own Control designs
Reversal
designs

Repeated measures & Randomized block
designs
24
Foundations of
Research
Basic Reversal design structure
“Normal” baseline state
Measurement or testing
Impose temporary
experimental condition
Measurement or testing
Return to normal state
Measurement or testing
Examples:
 Role of incentives in enhancing performance
 Impact of anti-depressant drug on mood
 Effect of self-awareness on following social
norms
25
Foundations of
Research
Core Assumptions of reversal designs:
Clear stimulus or condition -related hypothesis

Hypothesize that behavior is directly tied to a
condition or stimulus

Clear beginning and ending
 No “carry-over” effects (due to, e.g., learning,
sensitization, etc.)
 Changes in behavior only last as long as condition
is in place
 Changes can be induced and reversed more than
once
26
Foundations of
Research
Examples of reversal designs
Do men in bars change their drinking
rate to match that of an attractive
woman?
 A simple interview or questionnaire study
my not yield very accurate self-reports.
 If participants knew they were in an
experiment their behavior may change (an
experiment may be reactive).
 This may be tested in a structured, unobtrusive observation study.
 Provide a model in an actual bar, who systematically modulates her
drinking,
 Record drinking rates of targeted males in her vicinity.
27
Foundations of
Research
28
Examples of reversal designs
Test effect of, e.g., modeling (observation of attractive
experimental confederate) on alcohol consumption.
If the model influences
participant’s behavior:
50
45

40
35

30
25

20
15
10
5
0
h
ig
H
w
Lo
h
ig
H
w
Lo
Model's drinking rate
Consumption will increase
when the model’s does…
Rate goes back down when
model’s does.
Up again with model, etc..
Foundations of
Research
29
Examples of reversal designs
Test effect of, e.g., modeling (observation of attractive experimental confederate)
on alcohol consumption.
This is a basic Reversal Design;
 Test if a manipulation in the
model’s behavior induces change
in the participant,
50
45
40
 Then test whether that effect can
35
be reversed.
30
25
By inducing then reversing the
effect this design can show the
outcome to be closely tied to the
stimulus.
20
15
10
5
0
h
ig
H
w
Lo
h
ig
H
w
Lo
Model's drinking rate
Foundations of
Research
30
Reversal designs & carry-over
Reversal designs can test carry-over effects:
50
50
45
45
40
40
35
35
30
30
25
25
\
20
15
15
10
10
5
5
0
0
gh
Hi
w
Lo
gh
Hi
w
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gh
Hi
w
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Hi
w
Lo
Reversal effect: Modeling
controls drinking rate
\
20
Carry-over effect: Drinking rate
gradually increases over time
no matter what.
Foundations of
Research
Example:
single group
study with reversal design
The
Hawthorne
Study.
31
The Hawthorne Study is one of the more…
 Famous,
 Misunderstood & misused.
social research studies ever conducted
Context:
 Hawthorne Electrical Plant (Hawthorne Il.)
 1950s era
 Strong political fears of “Communist conspiracy”
 Unions seen as ‘fronts’ for international Communism.
 Working conditions terrible at Hawthorne, as in many companies.
 OSHA did not exist; worker’s rights largely absent
 Many Social Psychologists were imbued with the conservative
(anti-union) tenor of the times.
Foundations of
Research
Single group reversal design: The Hawthorne Study
Context (cont.):
 Union drive taking place
 Demands for better working conditions
 Productivity & quality decreasing
Study Purpose
32
Pre-Intervention
Baseline
assessment
1st Intervention
(change lighting)
 Increase motivation & productivity.
Hypothesis
 Employees simply want attention
 Any change in the work environment - even
a trivial one - leads to change.
Intervention
 Change inadequate lighting.
Study structure: Reversal design
1st Follow-up
assessment
2nd Intervention
(Reversal; change
the lighting back)
2nd Follow-up
assessment
Foundations of
Research
Single group reversal design: The Hawthorne Study
Outcomes
 Daily assembly line output
Findings
 Output rises after lighting improved (1st follow-up).
 Output rises again after lighting reversed to initial state.
 Thus:  Any change, even a negative one, “motivates” workers..
 Workers respond to simple attention, not real change.
Internal validity?
 Political bias: researchers hired to disprove workers’ claims!
 Mortality: as part of “union busting” dissatisfied workers fired
during study period.
 Reactive measures: workers fear for job may increase production,
not workplace change.
Sad legacy of the Hawthorne study
 The “Hawthorne effect” is commonly cited to discount demands
for change, or explain away positive findings of interventions.
33
Foundations of
Research
Within-subjects & blocking designs
Own Control designs
Reversal designs
 Repeated measures & Randomized
block designs
34
Foundations of
Research
35
Simple repeated measures / time series designs
Group
Measure1
M2
M3
M4
M5
M6…
Group2
Measure1
M2
M3
M4
M5
M6…
Group3
Measure1
M2
M3
M4
M5
M6…



Examine / describe changes over time in one or more
key variables.
Describe or test hypotheses about group differences
over time.
Groups may be assigned, in a true experiment.
 … intervention groups with long-term follow-up
 Groups may be measured or naturally occurring.
 … age, gender or ethnic groups.

Longer time-frame yields more valid & interpretable data.
Foundations of
Research
Is attention to childhood obesity causing it to decease?
2003  2012 data
 Older kids (2 19): no change
EXAMPLE
 Toddlers appear
to be doing
better.
 Supports
effectiveness of
recent infant
programs.
Click image for the article
Longer time frame: 1999  2012
 Older kids no still show no change
 Toddlers only look better because of a spike in 2003.
 Looking back to 1999 shows a flat line with lots of
variance.
36
Is attention to childhood obesity causing it to decease?
EXAMPLE
Foundations of
Research
Time series (or longitudinal) designs allow us to sensitively
measure change over time.
However, results can be sensitive to the specific time frame
being tested…
37
Foundations of
Research
Group
Interrupted time series design
Measure1
M2
M3
M4
M5
M6 …
Intervention
or event


Test effect of intervention or event on ongoing series of
measurements.
Intervention may be experimental or observed
 Policy shift, e.g., educational policy
 Uncontrolled event; e.g., 9/11/01, Media event
 Assessments may be experimental or archival
 Successive cross-sectional surveys
 Traffic data, clinic or crime reports, test scores
38
Foundations of
Research
Group
Measure1
M2
Multiple baseline


39
Time series designs
Demonstrate highly stable
effect
long-term crime rates
disease prevalence
economic performance…
Show steady rate of change
M3
M4
M5
M6 …
Intervention
or event
Hypothesis; tested by:

Shift in stable rate after
intervention

Increase / decrease in rate
of change after intervention
40
Example: “Natural” shift in a Baboon
culture toward cooperativeness.
Question:
Foundations of
Research
 Baboons are typically aggressive.
 Can a baboon troop develop and transmit a
learned “culture” of less aggression?
EXAMPLE
Data:
Shutterstock
Click for the original article in PLOS Biology.
 An ongoing, 20+ year observational study of a baboon troop.
• Assessments included detailed measures of aggressiveness and
other social behaviors, including cooperation.
Naturally occurring “Intervention”:

Tuberculosis infected Food was mistakenly left in a dumpster.

Animals competed with each other to feed on it (a free meal…).

The most dominant & aggressive males fed first;
 They were selectively infected…
 …and naturally culled from troop.
Foundations of
Research
41
Example: “Natural” shift in a Baboon culture toward
cooperativeness.
Question:
 Baboons are
 typically
It wouldaggressive.
be difficult (and unethical) for
culltransmit
dominant
 Can a baboonresearchers
troop developtoand
a males; it may not
be of
clear
and how many to take.
learned “culture”
lesswhich
aggression?
EXAMPLE
Data:
 The inadvertent introduction of Click
a limited
for the original article in PLOS Biology.
amount
of infected food
males to
 An ongoing, 20+
year observational
studyled
of adominant
baboon troop.
“self select” out of the troop.
• Assessments included detailed measures of aggressiveness and
other social behaviors, including cooperation.
Shutterstock
Naturally occurring “Intervention”:

Tuberculosis infected Food was mistakenly left in a dumpster.

Animals competed with each other to feed on it (a free meal…).

The most dominant & aggressive males fed first;
 They were selectively infected…
 …and naturally culled from troop.
42
Example: “Natural” shift in a Baboon culture toward
cooperativeness.
Foundations of
Research
Question:
 Can a baboon troop develop and transmit a
learned “culture” of less aggression?
Data:
EXAMPLE
 Ongoing, 20+ year observational study.
Shutterstock
Click for the original article in PLOS Biology.
“Intervention”:

Infected food selectively consumed by aggressive males.
Analysis:
Time
Time Series baseline
Baseline
With naturally
occurring “Intervention”
variance.
Observations extend into
the next generation
Aggression
Cooperation
Data simulated
Foundations of
Research
Example: “Natural” shift in a Baboon culture toward
cooperativeness.
43
Question:
 Can a baboon troop develop and transmit a
learned “culture” of less aggression?
EXAMPLE
Data:
Shutterstock
 Ongoing, 20+ year observational study.
Click for the original article in PLOS Biology.
 This data pattern shows the
 Very long-term data show
“Intervention”:
behavior of the troop to
the troop maintaining the
 change
Infectedafter
foodaselectively
by aggressive
males.
naturally consumedchange
into the
next
occurring event.
generation: clear evidence
Analysis:
of culture change.
Baseline
“Intervention”
Observations extend into
the next generation
Time
Aggression
Cooperation
Data simulated
Foundations of
Research
Example: “Natural” shift in a Baboon culture toward
cooperativeness.
44
Question:
Studies like this show the major virtue of repeated
 Can a baboon troop developmeasures:
and transmit a
learned “culture” of less aggression?
This finding is made possible only by virtue of multiple
Data:
EXAMPLE
naturalistic
over considerable time. Shutterstock
 Ongoing,
20+ yearobservations
observational study.
Click for the original article in PLOS Biology.
 This data pattern shows the
behavior of the troop to change
after a naturally occurring event.
Baseline
 Very long-term data show the troop
maintaining the change into the
next generation: clear evidence of
culture change.
“Intervention”
Observations extend into
the next generation
Time
Aggression
Cooperation
Data simulated
Foundations of
Research
Randomized block designs
Blocking Variable; between - subjects factor

“Person” variable; age, gender, ethnicity, etc.

Not a “true” IV since people not randomly assigned;
Or:

Experimental condition; drug dose, treatment, etc.
 “True” IV with random assignment
Repeated measure: within-subjects factor

Multiple treatment conditions: each participant is
observed in each treatment condition

(e.g., high v. low drug dose, different instructions…)
Or:
 Longitudinal / time sampling: measure D.V. over
multiple time periods (Cohort studies)
45
46
Within subjects designs; own control, 3
Foundations of
Research
Repeated measures / randomized block design
Group 1
Baseline
Measure
Control
Condition
Measure2
M3
M4..
Group 2
Baseline
Measure
Experimental
Condition
Measure2
M3
M4..
Assignment
Treatment. Primary
Randomly or via
natural “blocks”
Independent Variable.
Control group may receive
Placebo.
Baseline assessment
prior to intervention or
experimental condition.
Follow-up. Long-term
assessment of outcome or
Dependent Variable. Time may
represent 2nd Independent
Variable.
Example of Repeated Measure Design
Foundations of
Research
Question: Do Gay / bisexual men who use HIV
medications as Post-Exposure Prophylaxis [PEP]
have more risky sex.
Independent variables:
Medication use: Between subjects / Measured
“blocking” variable: men who request PEP v. those
who do not.
Study visit [time]: Repeated Measure [within
subjects]: Participants are interviewed every 6
months.
Dependent variable: % of men reporting risky sex at
any given time period.
47
Foundations of
Research
48
Unprotected anal intercourse (UAI) with HIV positive and
unknown sero-status partners among MSM, by study visit
Participants reporting (%)
60
60
Blocking
Blocking
variable
variable
50
50
40
40
PEP users
users
PEP
Non users
users
Non
30
30
20
20
All men get safer
over time
10
10
0
0
Repeated
Measure
(a Main Effect)
0
0
6
6
12
18
24
30
12
18
24
30
Month of study visit
*Adjusted for age, study site, drug use, and education
36
36
OR*(CI)
p
PEP
1.7(1.2-2.2)
.001
Visit
0.97( .96-.99) .001
Foundations of
Research
49
Unprotected anal intercourse (UAI) with HIV positive and
unknown sero-status partners among MSM, by study visit
Participants reporting (%)
60
60
Blocking
variable
50
50
40
40
PEP users
users
PEP
Non users
users
Non
30
30
20
20
Non-users of PEP
are generally safer
10
10
(also a Main Effect)
0
0
0
0
6
6
12
18
24
30
12
18
24
30
Month of study visit
*Adjusted for age, study site, drug use, and education
36
36
OR*(CI)
p
PEP
1.7(1.2-2.2)
.001
Visit
0.97( .96-.99) .001
Foundations of
Research
50
Unprotected anal intercourse (UAI) with HIV positive and
unknown sero-status partners among MSM, by study visit
Participants reporting (%)
60
60
Blocking
variable
50
50
40
40
PEP users
users
PEP
Non users
users
Non
30
30
20
20
10
10
0
0
Non-users at the end
of the study are safest
(an Additive Effect; time + group)
0
0
6
6
12
18
24
30
12
18
24
30
Month of study visit
*Adjusted for age, study site, drug use, and education
36
36
OR*(CI)
p
PEP
1.7(1.2-2.2)
.001
Visit
0.97( .96-.99) .001
Foundations of
Research
51
Unprotected anal intercourse (UAI) with HIV positive and
unknown sero-status partners among MSM, by study visit
Participants reporting (%)
60
60
Blocking
variable
50
50
40
40
PEP users
users
PEP
Non users
users
Non
30
30
20
20
10
10
0
0
There is no Interaction Effect:
The effect of time is the same for
both groups.
0
0
6
6
12
18
24
30
12
18
24
30
Month of study visit
*Adjusted for age, study site, drug use, and education
36
36
OR*(CI)
p
PEP
1.7(1.2-2.2)
.001
Visit
0.97( .96-.99) .001
HypotheticalHypothetical
Example
ofblock
a design,
randomized
block
randomized
1
design; basic clinical intervention / drug trial
Foundations of
Research
52
Question:
Effectiveness of different doses of an
anti-hypertensive drug
2nd questions:
Effects of: time, gender, medical status
of patients.
Population:
Hypertensive patients [systolic Bp > 145]
Outcome [D.V.]
Systolic Blood pressure
Blocking variable:
Drug dose: placebo v. 2 doses (high / low).
This IV carries the main hypothesis.
Repeated Measure:
Time. Key element of effectiveness:
stability x follow-up.
2nd Blocking vars.:
Gender, medical status, ethnicity, etc.
Foundations of
Research
53
Hypothetical randomized block design, 3
Systolic blood pressure
Example # 1 (hypothetical data): drug dose
& time on systolic Bp, complete sample
180
What does this pattern
of data show?
Placebo
170
Low Dose
160
High Dose
150
140
130

Main effects?

Interaction effects?

Hypothesis
supported?
120
110
100
Ba
s
3
el
in
e
M
on
6
th
M
on
9
th
M
on
12
th
M
on
th
Foundations of
Research
54
Hypothetical randomized block design, 4
Systolic blood pressure
Example # 2, hypothetical data: drug dose &
time on systolic Bp, complete sample
180
What does this pattern
of data show?
170
160
150

Main effects?
140

Interactions?

Hypothesis
supported?
130
Placebo
Low Dose
High Dose
120
110
100
Ba
s
3
el
in
e
M
on
6
th
M
on
9
th
M
on
12
th
M
on
th
Foundations of
Research
55
Hypothetical randomized block design, 5
Systolic blood pressure
Example # 3, hypothetical data: drug dose &
time on systolic Bp, complete sample
180

Main effects?

Interactions?

Hypothesis?
170
160
150
140
130
120
Placebo
Low Dose
High Dose
110
100
Ba
s
3
el
in
e
M
on
6
th
M
on
9
th
M
on
12
th
M
on
th
Foundations of
Research


Summary
Within subjects designs somewhat common in
psychological research;

Own control designs: create strong contrast for IV

Eliminate problems in creating experimental v. control
groups.
Very common in bio-medical or public health studies;

Most clinical studies are longitudinal; participants
followed over time

Intervention or experimental treatment is I.V. #1
(blocking or grouping variable).

Stability over time is I.V. # 2 (repeated measure)
56