© 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