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Independent variable (IV): Variable the experimenter manipulates - assumed to have a direct effect on the dependent variable.

Dependent variable (DV): Variable the experimenter measures, after making changes to the IV which are assumed to affect the DV.

Extraneous variables (Ex Vs): Other variables, apart from the IV, that might affect the DV. They might be important enough to provide alternative explanations for the effects, for example, confounding variables.

Laboratory experiment: Artificial environment with tight controls over variables.

Field experiment: Natural environment with independent variable manipulated by researchers.

Natural experiment: Natural changes in independent variable are used - it is not manipulated.

Note: In a true experiment participants are randomly allocated to groups.

Think of some examples from your course.

Which categories would the following studies fall into?

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Laboratory experiments

Strengths: Weaknesses:
Tighter control of variables. Easier to comment on cause and effect. Demand characteristics - participants aware of experiment, may change behaviour.
Relatively easy to replicate. Artificial environment - low realism.
Enable use of complex equipment. May have low ecological validity - difficult to generalise to other situations.
Often cheaper and less time-consuming than other methods. Experimenter effects - bias when experimenter's expectations affect behaviour.

A field experiment takes place anywhere in a natural setting; it could take place in a school, hospital, the street or an office.


A field experiment is an experiment; the independent variable is manipulated. Not all field studies are experiments.

Strengths: Weaknesses:
People may behave more naturally than in laboratory - higher realism. Often only weak control of extraneous variables - difficult to replicate.
Easier to generalise from results. Can be time-consuming and costly.
Strengths: Weaknesses:
Situations in which it would be ethically unacceptable to manipulate the independent variable. The independent variable is not controlled by the experimenter.
Less chance of demand characteristics or experimenter bias interfering. No control over the allocation of participants to groups (random in a 'true experiment').

Three experimental designs are commonly used:

Independent groups: Testing separate groups of people, each group is tested in a different condition.

Repeated measures: Testing the same group of people in different conditions, the same people are used repeatedly.

Matched pairs: Testing separate groups of people - each member of one group is same age, sex, or social background as a member of the other group.

In each case, there are one or more experimental groups, where the independent variable has changed and a control group where the independent variable has not changed.

Independent groups:

Avoids order effects. If a person is involved in several tests they man become bored, tired and fed up by the time they come to the second test, or becoming wise to the requirements of the experiment!

More people are needed than with the repeated measures design.

Differences between participants in the groups may affect results, for example; variations in age, sex or social background. These differences are known as participant variables.

Repeated measures:

Avoids the problem of participant variables.

Fewer people are needed.

Order effects are more likely to occur.

Matched pairs:

Reduces participant variables.

Avoids order effects.

Very time-consuming trying to find closely matched pairs.

Impossible to match people exactly, unless identical twins!


Counterbalancing: Alternating the order in which participants perform in different conditions of an experiment. For example, group 1 does 'A' then 'B', group 2 does 'B' then 'A' this is to eliminate order effects.

Randomisation: Material for each condition in an experiment is presented in a random order, this is also to prevent order effects.

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