What is an Experiment?
In an experiment, a researcher manipulates one or more variables,
while holding all other variables constant. By noting how
the manipulated variables affect a response variable,
the researcher can test whether a causal relationship exists
between the manipulated variables and the response variable.
Parts of an Experiment
All experiments have independent variables, dependent variables, and
- Independent variable. An independent variable
(also called a factor) is an explanatory
variable manipulated by the experimenter.
Each factor has two or more levels (i.e.,
different values of the factor). Combinations of factor
levels are called treatments. The table
below shows independent variables, factors, levels, and
treatments for a hypothetical experiment.
In this hypothetical experiment, the researcher is studying the
possible effects of Vitamin C and Vitamin E on health. There
are two factors - dosage of Vitamin C and dosage of Vitamin E.
The Vitamin C factor has three levels - 0 mg per day, 250 mg
per day, and 500 mg per day. The Vitamin E factor has 2 levels -
0 mg per day and 400 mg per day. The experiment has six
treatments. Treatment 1 is 0 mg of E and 0 mg of C, Treatment
2 is 0 mg of E and 250 mg of C, and so on.
- Dependent variable.
In the hypothetical experiment above, the researcher is looking
at the effect of vitamins on health. The dependent variable in
this experiment would be some measure of health (annual
doctor bills, number of colds caught in a year, number of
days hospitalized, etc.).
- Experimental units. The recipients of
experimental treatments are called experimental units.
The experimental units in an
experiment could be anything - people, plants, animals, or even
In the hypothetical experiment above, the experimental units would
probably be people (or lab animals). But in an experiment to measure
the tensile strength of string, the experimental units might be
pieces of string. When the experimental units are people, they are
often called participants; when the experimental units are animals,
they are often called subjects.
Characteristics of a Well-Designed Experiment
A well-designed experiment includes design features that
allow researchers to eliminate extraneous variables as an
explanation for the observed relationship between
the independent variable(s) and the dependent variable.
Some of these features are listed below.
- Control. Control refers to steps taken to
reduce the effects of extraneous variables (i.e., variables
other than the independent variable and the dependent
variable). These extraneous variables are called
Control involves making the experiment as similar as possible
for experimental units in each treatment condition. Three control strategies
are control groups, placebos, and blinding.
- Control group. A control group is a
baseline group that receives no treatment or a neutral
treatment. To assess
treatment effects, the experimenter compares results in
the treatment group to results in the control
- Placebo. Often, participants in an experiment respond
differently after they receive a treatment, even if the
treatment is neutral. A neutral treatment that
has no "real" effect on the dependent variable is called a
placebo, and a participant's positive response
to a placebo is called the placebo effect.
To control for the placebo effect, researchers often
administer a neutral treatment (i.e., a placebo) to the control
group. The classic example is using a sugar pill in drug
research. The drug is considered effective only if participants who
receive the drug have better outcomes than participants who
receive the sugar pill.
- Blinding. Of course, if participants in the
control group know that they are receiving a placebo,
the placebo effect will be reduced or eliminated; and
the placebo will not serve its intended control purpose.
Blinding is the practice of not telling
participants whether they are receiving a placebo. In this
way, participants in the control and treatment groups
experience the placebo effect equally. Often, knowledge
of which groups receive placebos is also kept from people who
administer or evaluate the experiment. This practice
is called double blinding. It prevents the
experimenter from "spilling the beans" to participants through
subtle cues; and it assures that the analyst's evaluation is not
tainted by awareness of actual treatment conditions.
- Randomization. Randomization refers to the
practice of using chance methods (random number tables, flipping a
coin, etc.) to assign experimental units to treatments.
In this way, the potential effects of lurking variables are
distributed at chance levels (hopefully roughly evenly)
across treatment conditions.
- Replication. Replication refers to the
practice of assigning each treatment to many experimental
units. In general, the more experimental units in each treatment
condition, the lower the variability of the dependent measures.
Confounding occurs when the experimental controls
do not allow
the experimenter to reasonably eliminate plausible alternative
explanations for an observed relationship between independent
and dependent variables.
Consider this example. A drug manufacturer tests a new cold
medicine with 200 participants - 100 men and 100 women.
The men receive the drug, and the women do not. At the end
of the test period, the men report fewer colds.
This experiment implements no controls! As a result,
many variables are confounded, and it is impossible to say
whether the drug was effective. For example, gender is
confounded with drug use. Perhaps, men are less vulnerable
to the particular cold virus circulating during the
experiment, and the new medicine had no effect at all. Or
perhaps the men experienced a placebo effect.
This experiment could be strengthened with a few controls.
Women and men could be randomly assigned to treatments. One
treatment group could receive a placebo, with blinding. Then, if
the treatment group (i.e., the group getting the medicine) had
sufficiently fewer colds than the control group, it would be
reasonable to conclude that the medicine was effective in
Test Your Understanding
Which of the following statements are true?
I. Blinding controls for the effects of confounding.
II. Randomization controls for effects of lurking variables.
III. Each factor has one treatment level.
(A) I only
(B) II only
(C) III only
(D) All of the above.
(E) None of the above.
The correct answer is (B). By randomly assigning experimental units to
treatment levels, randomization spreads potential effects of
roughly evenly across treatment levels.
ensures that participants in control and treatment conditions
placebo effect equally, but it does not guard against
confounding. And finally, each
or more treatment levels. If a factor had only one treatment
level, each participant in the experiment would get the same treatment
on that factor. As a result, that factor would be
confounded with every other factor in the experiment.