Sampling Methods

Explain random sampling

• all members of the target population have an equal chance of being selected

• Obtain a sampling population

• Allocate everyone a number

• Use random number generator

Explain systematic sampling

• quasi-random technique

• Every nth member of the target population is selected

Explain stratified sampling

• sample reflects the proportions of people in sub groups within the target population

• Population is divided into groups the researcher wants to represent

• Random sampling is used within each sub group

Explain opportunity sampling

• Researcher uses anyone who is willing/available to participate

Explain volunteer sampling

• participants select themselves to be part of the sample

Explain the terms target population and sample

• target population is the group that the findings apply to

• sample is the group who participate in the study, they are from the target population and should be representative

Explain the terms generalisation and bias

• Generalisation is the extent to which the findings / conclusions can be applied to the population

• Bias is created when groups over or under represent within the sample

What’s the key strength of random sampling

• free from researcher bias

• selection of participants in left by chance not the researcher

• random sample is more likely to be representative

What’s the key strength of opportunity sampling

• convenient

• Using available people is easier than using special procedures to choose

Why can stratified sampling be better than random sampling

• stratified sampling is more likely to be representative and generalisable

• stratified sampling has a reduced chance of random errors

• stratified sampling represents the population proportionally

What’s the key weakness of volunteer sampling

• volunteers might not be representative of population / attract a type of person - creating bias

What’s the key strength of systematic sampling

• more practical than random sampling