Chapter 3: Sampling Flashcards
What is sampling?
A process in selecting n from N to make inferences.
List the 7 reasons why samples are important.
- Cost-efficient
- Timely
- Efficient & Accurate
- Greater Scope
- Convenient
- Necessity
- Ethical Considerations
What is a census?
Interviewing all.
What is a non-probability sample?
Selected purposively or as volunteers where probabilities of selection are unknown which should be used for inferential statistics.
Non-probability Sampling Techniques
- Judgement Sampling
- Purposive Sampling
- Convenience Sampling
- Quota Sampling
- Snowball Sampling
What is a probability sample?
Randomly selected which requires a sampling frame where probabilities of selection are known, allowing valid generalizations about N.
Probability Sampling Techniques.
- Simple Random Sampling
- Systematic Sampling
- Stratified Sampling
- Cluster Sampling
Traditional probability sampling method.
Simple Random Sampling.
Formula for estimation in Simple Random Sampling.
Estimated Total = N times mean
Formula for mean of Simple Random Sampling.
Summation of x over n.
In this sampling technique, population is divided into L, allowing inferences for each subpopulation which increases precision of the estimates.
Stratified Random Sampling
In this sampling technique, there is a pattern in the selection of sample units.
Systematic Sampling
Formula for skipping pattern (k).
K=N/n
This sampling technique has subgroups whose elements are completely enumerated.
Cluster Sampling
A sampling technique where the set of original samples meet certain conditions and estimated population density and abundance.
Adaptive Sampling
A sampling technique for rare, mobile, and difficult to detect samples where observer moves along a set distance.
Line Transect Sampling or Distance Sampling
Most popular sampling technique for biological sciences which allows estimation of population size.
Capture-Recapture Sampling or Mark-Recapture Sampling
