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Every part of the population has the potential to be represented in the sample
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
Stratified random sampling
- Different layers of distinctly difference types of individuals.
- Samples are taken equally from each layer
Systematic random sampling
- Selecting individuals or clusters according to a predetermined sequence.
- Sequence must originate by chance
- 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
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
Takes people who are readily available – those that arrive on scene by mere happenstance
- 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
Snowball sampling (Referral sampling)
One person asks someone and then that people asks another
People or units are chosen for a particular purpose
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