# Sampling Techniques

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1. Probability Sampling
Every part of the population has the potential to be represented in the sample
2. Simple random sampling
• Every member of the population has an equal change of being selected.
• Not ideal for large populations like ALL 10-year olds or ALL lawyers
3. Stratified random sampling
• Different layers of distinctly difference types of individuals.
• Samples are taken equally from each layer
4. Systematic random sampling
• Selecting individuals or clusters according to a predetermined sequence.
• Sequence must originate by chance
5. Cluster sampling
• When population of interest is spread over large area. It may not be feasible to make up a list of every person living within the area from the list.
• Subdivide large area into smaller units (state to counties or city to precincts of school boundaries).
• It is important that clusters be similar with equal heterogeneous mix of individuals

For example, a city can be divided into 12 clusters, we randomly selected 4 clusters and then all their members become the sample
6. Non-Probability Sampling
The researcher has no way of predicting or guaranteeing that each element of the population will be represented in the sample.  Some members of the population have little or no change of being sampled
7. Convenience sampling
Takes people who are readily available – those that arrive on scene by mere happenstance
8. Quota sampling
• Need a certain number of people
• i.e. 20 African Americans
• Only regulates the size of each category within sample in every other respect, the selection of the sample is non-random and in most cases convenient
9. Snowball sampling (Referral sampling)