Sampling (17)

17. Sampling

There are various ways of sampling a population, according to research and analysis goals.

17.1. Stratified Sampling

Stratified sampling involves breaking the population into strata (a.k.a. subgroups) according to a specific identifiable characteristic in such a way that each member of the population belongs to only one strata. Stratified random sampling is the process of selecting independent simple random samples from each strata. A question that arises here Is how to allocate the sampling effort among the strata. There are various possibilities:

• Proportional allocation: The proportion of the sample from a stratum is the same as the proportion of that stratum to the population. This is used if there is little to nothing known about the population and there are no strong requirements for the production of information.
• Optimal allocation: More sample effort is allocated to strata with a higher population variance. This is used if the objective is to estimate an overall population parameter (such as mean, total, or proportion) as precisely as possible. This method is only optimal with this goal in mind.

Analysing the results of stratified random samples is relatively straightforward, and any stratum sample mean (m_j) can be used as an unbiased estimator of the population mean (μ_j). It can also be sued to estimate the population total, as this is the product of the population mean and the number of population members.

17.2. Other Ways to Sample

Various other sampling methods are:

• Cluster Sampling: This method can be used when a population can be subdivided into small geographical units, or clusters. A simple random sample of clusters is then selected, and each member of these clusters is contacted for data. Using this method very little prior information of the population is needed.
• Two-Phase Sampling: In this method the regular data-collection is preceded by a smaller pilot study, in which a smaller sample is used. This cost more time but allows for methods and procedures to be improved, and can provide some estimations for the true study.
• Non-random sampling: There are two main methods:
  
  • Non-probabilistic sampling: Sample members are selected by convenience. This often means that the sample is not representative of the population and lacks proper statistical validity.
  • Quota sampling: There are specified numbers of people of certain characteristics (race, age, gender etc.) that are contacted. This usually produces quite accurate estimates of population parameters, but it is not possible to determine the reliability of these estimates, because the sample was not randomly chosen.