Experimental designs in Psychology

Types of experimental designs

Independent groups (or 'independent measures'): 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.

Advantages and disadvantages for each experimental design.

Independent groups.

Strengths.

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.

Weaknesses.

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.

Strengths.

Avoids the problem of participant variables.

Fewer people are needed.

Weaknesses.

Order effects are more likely to occur.

Matched pairs.

Strengths.

Reduces participant variables.
Avoids order effects.

Weaknesses.

Very time-consuming trying to find closely matched pairs.

Impossible to match people exactly, unless identical twins.

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Observational studies

One of the simplest research methods, this simply involves observing and recording the behaviour that occurs. However, in order to make the process more scientific, a number of checks are often put in place.

BINTER-OBSERVER RELIABILITY: the extent to which there is agreement between two or more observers involved in observations of behaviour. A good study should have at least 80% agreement between observers.

Observation studies can be participant observations, where the researcher joins the group being studied, or non-participant observations, where the researcher stays apart and observes from a distance.

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Case studies

A case study involves a detailed investigation of a single individual or small group of individuals. Example of the type of research that would lend itself to a case study are investigations into the effects of a stroke on later personality and behaviour, studying the effects of severe deprivation and the possibilities for recovery and so on.

Case studies often involve the use of interviews with the individual and family, friends, medical professionals etc. They may continue for many years and for this reason are often expensive and time consuming. However, they may lead to the way to future research, as they will collect extremely in-depth data.

Correlation

Sometimes psychologists are interested in whether there is a relationship between two factors or variables, e.g. is there a relationship between how extrovert you are and how good at maths you are. In a case like this we might use a correlation.

In a correlation study the experimenter does not make any attempt to manipulate variables (so there is no IV or DV), he simply measures two things (e.g. maths scores and extroversion) and then compares them for a relationship (e.g. does it seem to be that as maths scores increase, so do extroversion scores).

The difference between an experiment and a correlation:

Experiment:
Shows cause and effect
Extraneous variables are controlled

Correlation:
Does not show cause and effect
No control of extraneous variables

Experiment Terminology

Ecological validity: The degree to which an investigation represents real-life experiences.
Experimenter effects: These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.
Demand characteristics: the clues in an experiment that lead the participants to think they know what the researcher is looking for (e.g. experimenter’s body language).
Independent variable (IV): Variable the experimenter manipulates (i.e. changes) – assumed to have a direct effect on the dependent variable.
Dependent variable (DV): Variable the experimenter measures.
Extraneous variables (EV) :are all variables, which are not the independent variable, but could affect the results (DV) of the experiment. EVs should be controlled were possible.
Confounding variables: Variable(s) that have effected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.