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© 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Chapter 9
Using Between-Subjects and Within-Subjects Experimental
Designs
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Experimental Design 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: Quantitatively (amount of exposure to
same variable) Qualitatively (use different exposures)
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Types of Experimental Designs
Between-Subjects Design Different groups of subjects are randomly
assigned to the levels of your independent variable
Data are averaged for analysis Within-Subjects Design
A single group of subjects is exposed to all levels of the independent variable
Data are averaged for analysis Single-Subject Design
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
Adapted from © 2005 The McGraw-Hill Companies, Inc..
The Problem of Error Variance Error variance is the variability among
scores not caused by the independent variable Error variance is common to all three
experimental designs Error variance is handled differently in each
design Sources of error variance
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 Taking steps to reduce error variance
Hold extraneous variables constant by treating subjects as similarly as possible
Match subjects on crucial characteristics Increasing the effectiveness of the
independent variable Strong manipulations yield less error variance than
weak manipulations (e.g. Greater increase in dosage of medication)
Randomizing error variance across groups 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..
Statistical analysis 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
I. Single-Factor Randomized Groups Design The randomized two-group design (see page 266)
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 Advantages include:
Simple, requires fewer subjects, no pretesting required, analysis is simple
Disadvantages include: 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.
The randomized multiple group design 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
Control Group Placebo Group Treatment Group
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Between-Subjects Designs Cont. II. Matched-Groups Designs (see page 270)
Steps 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 Distributes the characteristic evenly across treatments. Allows you to control subject variables that obscure results. May require fewer subjects
Disadvantages Less statistical power in analysis used which decreases ability
to detect differences.
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Between-Subjects Designs Cont.
The matched-pairs design Equivalent to the randomized multi-group design.
The matched multigroup design
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Within-Subjects Designs
Subjects are not randomly assigned to treatment conditions The same subjects are used in all conditions Closely related to the matched-groups design
Advantages Reduces error variance due to individual
differences among subjects across treatment groups
Reduced error variance results in a more powerful design
Effects of independent variable are more likely to be detected
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Disadvantages 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
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Sources of Carryover
Learning Learning a task in the first treatment may affect
performance in the second Fatigue
Fatigue from earlier treatments may affect performance in later treatments
Habituation Repeated exposure to a stimulus may lead to
unresponsiveness to that stimulus
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Sensitization Exposure to a stimulus may make a subject
respond more strongly to another Contrast
Subjects may compare treatments, which may affect behavior
Adaptation If a subject undergoes adaptation (e.g., dark
adaptation), then earlier results may differ from later ones
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Dealing With Carryover Effects Counterbalancing
The various treatments are presented in a different order for different subjects
May be complete or partial The Latin Square Design
Used when you make the number of treatment orders equal to the number of treatments
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Taking Steps to Minimize Carryover Techniques such as pre-training, practice
sessions, or rest periods between treatments can reduce some forms of carryover
Make Treatment Order an Independent Variable 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
A within-subjects design may be best when 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
Adding a second independent variable to a single-factor design results in a FACTORIAL DESIGN
Two components can be assessed The MAIN EFFECT of each independent variable
The separate effect of each independent variable Analogous to separate experiments involving those
variables The INTERACTION between independent
variables When the effect of one independent variable changes
over levels of a second
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Example of An Interaction
0
2
4
6
8
10
12
Level 1 Level 2
Level of Independent Variable A
Val
ue o
f th
e D
epen
dent
V
aria
ble
Level 1 Level 2
Adapted from © 2005 The McGraw-Hill Companies, Inc..
Higher-Order Factorial Designs
More than two independent variables are included in a higher-order factorial design As factors are added, the complexity of the
experimental design increases 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
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