1. Using Between-Subjects and Within-Subjects Experimental Designs
Chapter 9
Using Between-Subjects and Within-Subjects Experimental Designs

2. 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)

3. 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
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

4. 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

5. 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

6. 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

7. 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)

8. 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

9. 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.

10. Between-Subjects Designs Cont.
The matched-pairs design
Equivalent to the randomized multi-group design.
The matched multigroup design

11. 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

12. 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

13. Sources of Carryover Learning Fatigue Habituation
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

14. Subjects may compare treatments, which may affect behavior Adaptation
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

15. 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

16. 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

17. 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 S3 S4 S5 S6

18. 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

19. 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

20. Example of An Interaction

21. 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