References:
- Nursing Review Guide, 1st Edition, ISBN 978-621-02-2289-0, by Glenn Reyes Luansing
- A population is a general group to be studied. A sample refers to a selected portion of the population from whom the data will be collected. An element is an individual member of the sample population. The sampling frame is the listing of all elements.
Samples may be chosen in various ways to improve its representation of the population:
- Simple Random Sampling: every element of the population is given an equal chance or opportunity to be chosen as a sample. No bias.
- Stratified Random Sampling: done by first dividing the population into sub-strata or sub-populations according to some subject character (e.g. age), then applying random sampling from each sub-strata or sub-population.
- Systematic Random Sampling: involves utilizing some method to choose from the population randomly, most commonly selecting every nth element from the population.
- Cluster Random Sampling: sampling by sub-areas of the population, applicable if a population is spread geographically.
- Accidental/Convenience Sampling: a non-probability sampling technique that involves choosing samples from readily available groups accessible to the researcher.
- Purposive/Judgmental Sampling: a non-probability sampling technique that involves choosing elements based on common knowledge or as a typical choice.
- Snowball Sampling: a non-probability sampling technique where the researchers use networking or referrals from previous elements to acquire more data, e.g. referrals of one cancer patient to a fellow cancer patient who underwent the same treatment.
- Quota Sampling: a non-probability sampling technique where the population is divided into subpopulations, and chosen based on “other” personal criteria instead of random sampling.