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Between-Subjects Experiments

In a between-subjects experiment, each participant is tested in only one condition. For example, a

researcher with a sample of 100 university students might assign half of them to write about a traumatic

event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe

agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments

for that disorder. It is essential in a between-subjects experiment that the researcher assigns participants

to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma

condition and a neutral condition, for example, should include a similar proportion of men and women, and

they should have similar average IQs, similar average levels of motivation, similar average numbers of health

problems, and so on. This matching is a matter of controlling these extraneous participant variables across

conditions so that they do not become confounding variables.

matched-groups

An alternative to simple random assignment of participants to conditions is the use of a matched-groups

design. Using this design, participants in the various conditions are matched on the dependent variable

or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that

these variables will not be confounded across the experimental conditions. For instance, if we want to

determine whether expressive writing affects people's health then we could start by measuring various

health-related variables in our prospective research participants. We could then use that information

to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest

participants would be randomly assigned to complete different conditions (one would be randomly assigned

to the traumatic experiences writing condition and the other to the neutral writing condition). The next two

healthiest participants would then be randomly assigned to complete different conditions, and so on until

the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at

the beginning of the study. If at the end of the experiment, a difference in health was detected across the

two conditions, then we would know that it is due to the writing manipulation and not to pre-existing

differences in health.

Within-Subjects Experiments

In a within-subjects experiment, each participant is tested under all conditions. Consider an experiment

on the effect of a defendant's physical attractiveness on judgments of his guilt. Again, in a between-subjects

experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt,

and another group of participants would be shown an unattractive defendant and asked to judge his guilt.

In a within-subjects experiment, however, the same group of participants would judge the guilt of both an

attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant

variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number

of siblings, and so on-because they are the very same people. Within-subjects experiments also make it

possible to use statistical procedures that remove the effect of these extraneous participant variables on

the dependent variable and therefore make the data less "noisy" and the effect of the independent variable

easier to detect. We will look more closely at this idea later in the book. However, not all experiments can

use a within-subjects design nor would it be desirable to do so.

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in

one condition at a time. There is another approach, however, that is often used when participants make

multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive

defendants and 10 unattractive defendants. Instead of having people make judgments about all 10

defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20

defendants in a sequence that mixed the two types. The researcher could then compute each participant's

mean rating for each type of defendant. Or imagine an experiment designed to see whether people with

social anxiety disorder remember negative adjectives (e-g., "stupid" "incompetent") better than positive ones

(e.g., "happy" "productive"). The researcher could have participants study a single list that includes both

kinds of words and then have them try to recall as many words as possible. The researcher could then count

the number of each type of word that was recalled.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects

design. This possibility means that researchers must choose between the two approaches based on their

relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing

time per participant. They also avoid carryover effects without the need for counterbalancing. Within-

subjects experiments have the advantage of controlling extraneous participant variables, which generally

reduces noise in the data and makes it easier to detect any effect of the independent variable upon the

dependent variable. Within-subjects experiments also require fewer participants than between-subjects

experiments to detect an effect of the same size.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper

counterbalancing) in the time that is available per participant-and you have no serious concerns about

carryover effects-this design is probably the best option. If a within-subjects design would be difficult

or impossible to carry out, then you should consider a between-subjects design instead. For example, if

you were testing participants in a doctor's waiting room or shoppers in line at a grocery store, you might

not have enough time to test each participant in all conditions and therefore would opt for a between-

subjects design. Or imagine you were trying to reduce people's level of prejudice by having them interact

with someone of another race. A within-subjects design with counterbalancing would require testing some

participants in the treatment condition first and then in a control condition. But if the treatment works and

reduces people's level of prejudice, then they would no longer be suitable for testing in the control condition.

This difficulty is true for many designs that involve a treatment meant to produce long-term change in

participants' behavior (e.g,, studies testing the effectiveness of psychotherapy). Clearly, a between-subjects

design would be necessary here.