Collecting Data

Question 1

Simple Random Sampling

Answer 1

A random selection from an identifiable population, where each member of the sample frame has an equal chance of being selected eg. Using a random number generator
- no bias
- not suitable for large populations

Question 2

Systematic sampling

Answer 2

Population is listed in a particular way and then a simple rule is used to choose people eg. Every 10th member of the sample frame
- simple and quick
- suitable for large populations
- can introduce bias

Question 3

Stratified sampling

Answer 3

Population divided into groups (strata), which are likely to behave differently and then simple random sampling is carried out in each strata. Sample size / population sizes to guarantee a proportional response.
- reflects population structure
- there must be distinct subsects within the population

Question 4

Quota sampling

Answer 4

Putting population into groups and sampling a given number of items from each group.
- reflects the proportions within the population
- doesn’t require a sampling frame
- biased

Question 5

Opportunity sampling

Answer 5

Sample is taken from the people who are available, and willing to take part.
- biased
- easy

Question 6

Cluster sampling

Answer 6

Divide a population into clusters and randomly selecting clusters to be sampled.
- simple
- efficient
-prone to bias