Experimental designs are often said to be the best approach for obtaining information about causal relationships, allowing researchers to assess the correlation (relationship) between one variable (a measurable characteristic of a sample) and another, for example, the effect on teenage pregnancy rates following an educational programme. . A principal factor of such designs it that one element is manipulated on purpose to see whether it has any impact upon another measure. The element or factor that is being manipulated (e.g. the education package) is known as the independent variable, whereas the change (or outcome) resulting from the implementation of the independent variable (e.g. teenage pregnancy rates) is the dependent variable.

Hypotheses

Experimental designs are developed to answer hypotheses, or testable statements, formulated to answer specific questions, for example, 'is family therapy an effective means of helping children with mental health problems?'. The investigators set up an experimental study (for example, a randomised controlled trial, quasi-experimental or single case design study, see below) and collect and analyse data, which will support or disprove the hypothesis accordingly.

Hypotheses can be based on theory, on earlier research findings or on a 'hunch' that the researcher may wish to examine further. A two-tailed hypothesis refers to those testable statements that can go in one of two directions. For example, 'Is there a difference in the outcome of people with mental health problems who are given a drug compared to psychotherapy?'. Alternatively, investigators may decide to employ a one-tailed hypothesis, for which the direction of the outcome would be pre-stated, such as a prediction that mental health patients receiving psychotherapy would fare better than those simply treated with medication.

When testing a hypothesis, it is a research convention to hypothesise that no difference will occur. This is defined as the 'null hypothesis'. For example, at the start of a study, a researcher might produce a null hypothesis stating that family therapy is no better than medication or standard care for children with mental health problems. This is then tested via an interventional study and if one intervention demonstrates superiority over another, the null hypothesis can be rejected.

Extraneous and confounding variables

Extraneous variables constitute all factors, other than the independent variable, that have an effect on the dependent variable (the possible effects of an intervention). Since an underlying aim of the experimental approach is that alterations in the independent variable (the variable manipulated by a researcher - such as the introduction of a new service) lead to changes in the dependent variable, it is essential that any extraneous variables are considered during the planning stages of a study.

Potential extraneous variables should not prejudice the relationship between the independent and dependent variables. Extraneous variables that have such an unwanted effect are known as 'confounding variables'. Confounding variables lead to ambiguous results because they may have been influenced by additional factors to the independent variable. In an experiment, every effort is made to control for confounding variables in order to be more confident of the cause-effect relationship. In the case of the early pregnancy preventative package referred to above, the researchers will be keen to know whether any decrease (or increase) in pregnancy rates are due to the new intervention and not to alternative factors (e.g. family background, educational levels, ethnicity, personality). It is important to realise that extraneous variables may not necessarily be confounding and it may prove impossible to account for all extraneous variables. Regardless, researchers should take steps to ensure that variables do not become confounding, thus reducing the legitimacy of claims made about the relationship between dependent and independent variables.

Extraneous variables may be of three kinds:

• Participant variables "“ e.g. gender, being left or right handed, IQ.
• Investigator variables "“ e.g. different investigators collecting data from different respondents, who may get different results because of their gender, ethnic background, etc.
• Situational variables "“ e.g hunger, fatigue or other environmental factors.

Internal and External Validity

An experimental approach to investigation is said to be an effective means of strengthening:

• Internal Validity "“ which relates to how far the study has established whether a variable or condition under scrutiny has had an effect. Controlling against extraneous variables strengthens internal validity.
• External Validity "“ which relates to whether findings from a specific sample involved in a study can be generalised to a larger, target population.

Laboratory versus field experiments

A difference needs to be made between laboratory and field experiments. The former take place in an artificial setting, with the advantage of being able to control precisely for confounding variables. Laboratory experiments allow for the setting up of artificial situations that would not occur in everyday life and for ease of replication if other researchers wished to conduct a similar investigation. However, the types of variables and situations that can be studied under laboratory conditions are restricted, and the degree of artificiality makes the generalisation of findings to real life more problematic. In addition, the setting may shape how people behave. They may behave in a way that they believe is required by investigators (demand characteristics). The unfamiliar environment used in laboratory experiments may again shape the way that people react, giving untrue results. Field experiments, in contrast to those conducted in a laboratory, have good ecological validity, i.e. they can be transferred more easily to other, real life settings because they take place in familiar environments, such as a school, clinic, day care centre. In such studies, it is also easier to hide from participants the fact that they are being studied, hence reducing the risk of demand characteristics. Unfortunately, confounding variables are less easily controlled in field settings compared to those performed in a laboratory.

Operationalising definitions

In experimental research it is important that the researcher is clear about what they are doing from the outset. Therefore, before embarking on an experiment, researchers must have 'operationalised' the variables (independent and dependent) that are to be measured. For example, when measuring the effectiveness of an intervention to improve the quality of life for older people in the community, how is quality of life to be measured? What aspects of this rather abstract concept are to be considered in assessing whether any form of progress or change has been established? One way is to use a standardised measure of quality of life, complemented by semi-structured interviews, in which participants are asked to define for themselves whether or not they feel an intervention has helped and, if so, what specific areas of their life have improved. A clear definition of concepts involved in the experiment not only ensures its validity, it also increases the chances that it could be replicated by others wishing to carry out a follow up or similar investigation.