Forest Inventory Glossary Stratified sampling is actually not a new sampling design of its own, but a procedural method to subdivide a population into separate and more homogeneous sub-populations called strata Kleinn . In some situations it is useful from a statistical point of view, or required for practical and organizational reasons, to subdivide the population in different strata.
For example, geographical regions can be stratified into similar regions by means of some known variable such as habitat type, elevation or soil type.
Another example might be to determine the proportions of defective products being assembled in a factory. In this case sampling may be stratified by production lines, factory, etc. Can you think of a couple additional examples where stratified sampling would make sense?
Look for opportunities when the measurements within the strata are more homogeneous. The principal Stratified random sampling for using stratified random sampling rather than simple random sampling include: Stratification may produce a smaller error of estimation than would be produced by a simple random sample of the same size.
This result is particularly true if measurements within strata are very homogeneous. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings. Estimates of population parameters may be desired for subgroups of the population. These subgroups should then be identified.
|Stratified Random Sampling | Mathstopia||Example[ edit ] Assume that we need to estimate average number of votes for each candidate in an election.|
The county has two towns, A and B, and a rural area C. Town A is built around a factory and most households contain factory workers with school-aged children. There are households in town A, 62 in town B and 93 in the rural area, C.
The firm decides to select 20 households from Town A, 8 households from Town B and 12 households from the rural area. The results are given in the following table: N in the output denotes numbers of data Usually a sample is selected by some probability design from each of the L strata in the population, with selections in different strata independent of each other.
The special case where from each stratum a simple random sample is drawn is called a stratified random sample. Does it make sense to use a stratified random sample for this problem? Why or Why not? Yes, for all three reason listed above. Since selections in different stratum are independent, the variance is: For stratified random sampling, i.3.
stratified sampling 4. cluster sampling 5. (quota sampling) 6. (convenience sampling) 7. (judgment sampling) simple random sampling. a sampling procedure in which each member of the population has an equal probability of being included in the sample.
systematic sampling. Stratified Random Sampling. Stratified Random Sampling is a sampling method (a way of gathering participants for a study) used when the population is composed of several subgroups that may differ in the behavior or attribute that you are studying.
sampling is typically considered to be a random sampling method, as long as the starting point is random and the periodic interval of selecting participants is determined before sampling takes place.
A simple random sample is similar to a random sample. The difference between the two is that with a simple random sample, each object in the population has an equal chance of being chosen. With random sampling, each object does not necessarily have an equal chance of being chosen.
Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata.
Page 74 Table Estimates from an optimally allocated stratified simple random sample (n = 8); the Province’91 population. NOTE: In this data set, the fpc changes with the strata. This is different from all of the previous examples.