Random Sampling

It is practically impossible to get a truly random sample. In a random sample every member of your target population would have an equal chance of being selected. So for example if you wanted a random sample of students from GIS you would need to obtain all of their names, put them in a hat and draw your sample out. In actual fact that would be the easy bit. The difficult task would be finding them and persuading their parents to let your chosen ones take part!


Opportunity Sampling

Opportunity sampling is less a mathematical method of sampling, and more of a “pick whoever is available” approach. Researchers will use whoever they can find who is filling to take part in the study. The ways in which the participants are chosen are not structured. An example might be someone doing questionnaires in a town high street. They will probably not have a specific participant in mind, but instead will just attempt to use everyone, and will happily include anyone who agrees to take part in the findings.

Volunteer (or self-selected sampling)

Volunteer sampling calls for volunteers to willingly contribute their time for the study. They may respond to a letter inviting a number of people to participate, or more commonly might respond to an advertisement, which often involves payment also.

Stratified Sampling

Stratified Sampling is used to ensure that certain groups are all represented by the sample. The researcher will decide what specific groups need to be tested within the sample, and will calculate how many people should be selected from each group using proportions. If you are investigating obedience in males and females between the ages of 20 and 40, you may separate them into four categories (males 20-29, males 30-40, females 20-29, females 30-40). If there were four times as many men as women, you would sample four times as many males.