Chapter 9
Using Between-Subjects and WithinSubjects Experimental Designs
© 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Experimental Design
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Used when your goal is to establish causal
relationships between variables and you can
manipulate variables.
To manipulate the independent variable you
set its value to at least two different values
(levels).
You can manipulate it:
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Quantitatively (amount of exposure to same
variable)
Qualitatively (use different exposures)
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Types of Experimental Designs
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Between-Subjects Design
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Within-Subjects Design
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Different groups of subjects are randomly assigned to
the levels of your independent variable
Data are averaged for analysis
A single group of subjects is exposed to all levels of
the independent variable
Data are averaged for analysis
Single-Subject Design
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Single subject, or small group of subjects is (are)
exposed to all levels of the independent variable
Data are not averaged for analysis; the behavior of
single subjects is evaluated
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The Problem of Error Variance
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Error variance is the variability among scores
not caused by the independent variable
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Error variance is common to all three
experimental designs
Error variance is handled differently in each
design
Sources of error variance
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Individual differences among subjects
Environmental conditions not constant across
levels of the independent variable
Fluctuations in the physical/mental state of an
individual subject
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Handling Error Variance
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Taking steps to reduce error variance
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Increasing the effectiveness of the independent
variable
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Hold extraneous variables constant by treating subjects as
similarly as possible
Match subjects on crucial characteristics
Strong manipulations yield less error variance than weak
manipulations (e.g. Greater increase in dosage of
medication)
Randomizing error variance across groups
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Distribute error variance equivalently across levels of the
independent variable
Accomplished with random assignment of subjects to
levels of the independent
Adapted from © 2005 The McGraw-Hill Companies, Inc..
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Statistical analysis
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Random assignment tends to equalize error variance
across groups, but not guarantee that it will
You can estimate the probability that observed
differences are due to error variance by using
inferential statistics
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Between-Subjects Designs
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I. Single-Factor Randomized Groups Design
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The randomized two-group design (see page 266)
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Randomly assign to two groups (experimental and control)
Expose the two groups to different levels of indep. Variable
Hold extraneous variables constant
Compare the two means
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Advantages include:
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Simple, requires fewer subjects, no pretesting required, analysis is
simple
Disadvantages include:
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Provides limited amount of information about effect of independent
variable (see example page 267 text)
Limited sensitivity to effect when subjects differ greatly in
characteristics that influence their performance on dep. measure
Limited at detecting limits of an effect (need more levels)
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Between-Subjects Designs Cont.
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The randomized multiple group design
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Additional levels of the independent variable can be added to
form a MULTIGROUP DESIGN
If different levels of the independent variable represent
quantitative differences, the design is a PARAMETRIC DESIGN
If different levels of the independent variable represent
qualitative differences, the design is a NONPARAMETRIC DESIGN
When you manipulate your indep variable quantitatively you are
using a parametric design
Parametric- refers to the systematic variation of the amount of
the independent variable.
A variation of this method/design is the multiple control group
design
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Control Group
Placebo Group
Treatment Group
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Between-Subjects Designs Cont.
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II. Matched-Groups Designs (see page 270)
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Steps
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Obtain a sample of subjects
Measure the subjects for a certain characteristic (e.g., intelligence)
that you feel may relate to the dependent variable
Match the subjects according to the characteristic (e.g., pair
subjects with similar intelligence test scores) to form pairs of
similar subjects
Randomly assign one subject from each pair of subjects to the
control group and the other to the experimental group
Carry out the experiment in the same manner as a randomized
group experiment
Advantages
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Distributes the characteristic evenly across treatments.
Allows you to control subject variables that obscure results.
May require fewer subjects
Disadvantages
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Less statistical power in analysis used which decreases ability to
detect differences.
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Between-Subjects Designs Cont.
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The matched-pairs design
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Equivalent to the randomized multi-group design.
The matched multigroup design
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Within-Subjects Designs
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Subjects are not randomly assigned to treatment
conditions
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The same subjects are used in all conditions
Closely related to the matched-groups design
Advantages
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Reduces error variance due to individual differences
among subjects across treatment groups
Reduced error variance results in a more powerful
design
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Effects of independent variable are more likely to be detected
Adapted from © 2005 The McGraw-Hill Companies, Inc..
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Disadvantages
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More demanding on subjects, especially in complex
designs
Subject attrition is a problem
Carryover effects: Exposure to a previous treatment
affects performance in a subsequent treatment
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Sources of Carryover
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Learning
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Fatigue
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Learning a task in the first treatment may affect
performance in the second
Fatigue from earlier treatments may affect
performance in later treatments
Habituation
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Repeated exposure to a stimulus may lead to
unresponsiveness to that stimulus
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Sensitization
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Contrast
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Exposure to a stimulus may make a subject respond
more strongly to another
Subjects may compare treatments, which may
affect behavior
Adaptation
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If a subject undergoes adaptation (e.g., dark
adaptation), then earlier results may differ from later
ones
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Dealing With Carryover Effects
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Counterbalancing
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The various treatments are presented in a different
order for different subjects
May be complete or partial
The Latin Square Design
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Used when you make the number of treatment orders
equal to the number of treatments
Adapted from © 2005 The McGraw-Hill Companies, Inc..
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Taking Steps to Minimize Carryover
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Techniques such as pre-training, practice sessions,
or rest periods between treatments can reduce
some forms of carryover
Make Treatment Order an Independent
Variable
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Allows you to measure the size of carryover
effects, which can be taken into account in future
experiments
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Example of a Counterbalanced
Single-Factor Design With Three
Treatments
Subjects
First
Treatment
Administered
Second
Treatment
Administered
Third
Treatment
Administered
S1
1
2
3
S2
1
3
2
S3
2
1
3
S4
2
3
1
S5
3
1
2
S6
3
2
1
Adapted from © 2005 The McGraw-Hill Companies, Inc..
When to Use a Within-Subjects
Design
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A within-subjects design may be best when
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Subject variables are correlated with the dependent
variable
It is important to economize on participants or subjects
You want to assess the effects of increasing exposure on
behavior
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Factorial Designs
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Adding a second independent variable to a singlefactor design results in a FACTORIAL DESIGN
Two components can be assessed
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The MAIN EFFECT of each independent variable
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The separate effect of each independent variable
Analogous to separate experiments involving those variables
The INTERACTION between independent variables
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When the effect of one independent variable changes over
levels of a second
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Example of An Interaction
Value of the Dependent
Variable
Level 1
Level 2
12
10
8
6
4
2
0
Level 1
Level 2
Level of Independent Variable A
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Higher-Order Factorial Designs
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More than two independent variables are
included in a higher-order factorial design
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As factors are added, the complexity of the
experimental design increases
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The number of possible main effects and interactions
increases
The number of subjects required increases
The volume of materials and amount of time needed to
complete the experiment increases
Adapted from © 2005 The McGraw-Hill Companies, Inc..