You will need to know everything on this page and be able to recall and apply the information here to all the studies.
These take place in either a lab or in a controlled environment setting, which is unnatural for the participants. They attempt to control all variables except the IV.
By changing one variable (the IV) while measuring another (the DV) while we control all others, as far as possible, then the experimental method allows us to draw conclusions with far more certainty than any non-experimental method.
If the IV is the only thing that is changed then a cause and effect relationship can be found between the IV ad the DV.
Sometimes it is possible to carry out experiments in a more natural setting, i.e. in ‘the field ’. As with the laboratory experiment, the independent variable is still deliberately manipulated by the researcher.
However it is not possible to have such tight control over variables in the field, but it does have the advantage of being far less artificial than the laboratory.
Quasi experiments may take place in the lab or field. Like other experiments they have an IV but in this type of experiment the experimenter does not directly manipulate the IV.
Some IVs are not open to manipulation as some conditions are pre-decided by fixed characteristics. E.g. comparing men and women’s driving skills, they cannot be randomly allocated to be male or female. The IV is naturally occurring. Other examples of pre-existing variables might be age, IQ, position in the family and social background.
Aim – a general statement about the purpose of an investigation.
Experimental Hypothesis – a precise, testable statement about the expected outcome of the experiment.
A hypothesis must be:
Is not really a true method of sampling because it means taking whoever is available. Researchers take whoever they can find to take part. The way participants are selected is not systematic or structured. Psychology students tend to use opportunity sampling as they have limited access to participants
It tends to be more ethical because the researcher can judge if the participant is likely to be upset by the study or is too busy to take part.
The researcher has more control over who is asked, so finding participants should be quick and efficient and costs less money, for example the researcher may use friends, family or colleagues.
The people who are available at the time may well not be representative of the target population as a whole, so the sample will be biased.
The researcher may have more control over who is chosen and choose certain people, leading to a biased sample.
Stratified sampling involves classifying the population into categories and then choosing a sample which consists of participants from each category in the same proportions as they are in the population. For example, if you wanted to carry out a stratified sample of students from a sixth form college you might decide that important variables are sex, 1st or 2nd years, age, have a part-time job and so on. You could then identify how many participants there are in each of these categories and choose the same proportion of participants in these categories for your study.
Stratified sampling is an efficient way of ensuring that there is representation from each group. Random sampling would probably still provide some participants from each group, but the researcher cannot be sure of this and may therefore need a larger sample. Stratified sampling limits the numbers needed to obtain representation from each group.
However, stratified sampling can be very time consuming as the categories have to be identified and calculated. As with random sampling, if you do not have details of all the people in your target population you would struggle to conduct a stratified sample.
Here every member of the target population has an equal chance of being selected. Everyone in the target population is available for selection each time a participant is picked out.
It could be done by drawing straws or pulling names from a hat. One popular method is to give each member of the target population a number and then to take numbers from a random number table.
There is no bias in the way that the participants are selected, everyone has an equal chance of being selected. Therefore the sample is likely to be representative of the target population.
Random sampling can be very time consuming and is often impossible to carry out, particularly when you have a large target population, of say all students. For example if you do not have the names of all the people in your target population you would struggle to conduct a random sample.
Other issues people may not be available on the day, or they simply do not wish to take part in the study so there could be bias.
Made up of participants chosen mathematically. This is done by taking every nth person in the sampling frame.
It avoids bias as, once the researcher has decided what number they are going to use for selection, they have no control over who is selected.
The law of probability says that the researcher will normally get a representative sample. For example, what is the chance that every fifth person is a male if the list of people is 50% male and 50% female?
Fairly simple procedure
There is a chance, although unlikely, of a ‘freak’ sample which would not be representative.
It is not as objective as random sampling, because the researcher may decide on how people are listed before selection and on what number to use for the ‘system’