Chapter 9
1
Within & Between designs
Within Subjects
Students
A
B
C
D
E
Phonics
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15
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Whole
Word
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14
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Between Subjects
Students Phonics Whole
Word
A
B
C
D
E
F
G
H
I
J
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15
14
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repeated- measures

A within- subjects experimental design,
also known as a repeated- measures
experimental design, compares two or
more different treatment conditions ( or
compares a treatment and a control) by
observing or measuring the same group
of individuals in all of the treatment
conditions being compared.
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Advantages of Within- Subjects
Designs
2 problems are reduced or eliminated in a
within- subjects design.
•
•
1. Group differences
2. High variance
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Within subjects
a within- subjects design is generally more
powerful than a between- subjects design;
that is, a within- subjects design is more
likely to detect a treatment effect than a
between- subjects design.
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Threats to internal validity for
within- subjects designs
1- Confounding from environmental
variables (room difference, light
difference, temperature difference)
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2- Confounding from timerelated factors.
•
History.
• Maturation..
• Instrumentation.
• Testing effects.
• Statistical Regression
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3- participant attrition

Another potential problem for the withinsubjects design is participant attrition.
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4- Order Effects
•
•
•
Carryover effect
Contrast effect (lighting in the cinema)
Progressive error
• Practice effect
• Fatigue effect
• Reduced motivation effect
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Dealing with time- related threats
and order effects
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1- Controlling Time
•
if the different treatment conditions are
scheduled over a period of months, the
chances greatly increase that an outside
event ( history, maturation, or change in the
measurement instrument) will have an
influence on the results.
•
However if the time between treatments is
too short other factors such as fatigue or
reduced motivation may change the results
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2- Switch to a Between- Subjects
Design

In some situations, order effects are so
strong and so obvious that a researcher
probably would not even consider using
a within- subjects design. For example,
a within-subjects design is a poor choice
for a study comparing two methods of
teaching reading to first- grade children.
After the children have been taught with
method I, they are permanently
changed.
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3- Counterbalancing

The process of matching treatments with
respect to time is called
counterbalancing.
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Easy Case
Order effects evenly distributed between
the treatment conditions.
It doesn’t matter which treatment comes
first.
There is a constant (e.g., d=5 points)
change due to order effect
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No Order
effect
Pop Music
20
23
25
19
26
17
14
16
Classic Music
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29
26
31
22
20
24
20 26
26-20 =6
Order
effect
Method A Method B
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23
25
19
26
17
14
16
32 (27+5)
34 (29+5)
34 (29+5)
31 (26+5)
36 (31+5)
27 (22+5)
25 (20+5)
29 (24+5)
20 31
31-20 =11
Counter
Balanced
Method A
Method B
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23
25
19
31 (26+5)
22 (17+5)
19(14+5)
21 (16+5)
32 (27+5)
34 (29+5)
34 (29+5)
31 (26+5)
22.5
31
22
20
24
28.5
28.5-22.5 =6
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Limitations of Counterbalancing
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Limitation 1
Counterbalancing balances the effect of
ordering effect but it doesn’t eliminate it.
So both means are inflated
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Limitation 2
A more serious problem is when
counterbalancing adds order effect to
some of the individuals but not to all.
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Limitation 3
When the order effect is not symmetrical
•
One treatment might produce a larger
order effect than the other treatment
• (Math & Statistics)
•
In such situations, the order effects are not
symmetrical, and counterbalancing the
order of treatments does not balance the
order effects.
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Limitation 4
Number of treatments
With only two treatment conditions,
complete counterbalancing is easy: There
are only two possible sequences.
However, as the number of treatments
increases, complete counterbalancing
becomes more complex.
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Number of Treatments
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Partial counter-balancing

One solution to this problem is to use
what is known as partial counterbalancing.

A simple and unbiased procedure for
selecting sequences is to construct a
Latin square.
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Latin square
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Limitation of Latin square
The Latin square is not a perfect example of partial counterbalancing because it does
not balance every possible sequence of treatment conditions. For example, the first
three groups all receive treatment A followed immediately by treatment B.
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Random order
One method for improving the square is to
use a random process to rearrange the
columns ( for example, a coin toss to
decide whether or not each column is
moved)
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Statistical analyses
With two treatment conditions,
a repeated- measures t test

For more than 2 treatments a
single-factor ANOVA ( repeated measures)
can be used to evaluate the statistical
significance of the mean difference.

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Ordinal & nominal scale

If the data are measured on an ordinal
scale ( or can be rank ordered), a
Wilcoxon test can be used to evaluate
significant differences.

If the data includes only positive and
negative (nominal) effects then we use a
sign test.
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Matched- Subjects Designs

In a matched- subjects design, each
individual in one group is matched with a
participant in each of the other groups.

The goal of a matched- subjects design
is to duplicate all the advantages of
within- and between- subjects designs
without the disadvantages of either one.
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Group Discussion - Ch8-9

Describe some of the problems that can
develop if participants from different
treatment conditions have an
opportunity to talk with each other during
the course of the experiment.​
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