When would one use probability sampling? Explain and indicate when each type would be preferred.

Question

What will be an ideal response?

Answer 1

Simple Random Sampling: Use random sampling when the researcher has a list of all elements in the population, numbers the elements in the sampling frame, and uses systematic procedure for picking corresponding numbers from a random number table to choose sample.
Systematic Random Sampling: Variant of simple random sampling but is little less time consuming. From the list of population members in the sampling frame, choose first element randomly using random numbers table then select every nth element thereafter.

Stratified Random Sampling: All elements in population (sampling frame) are differentiated on the basis of their value on some relevant characteristic to form strata. Next, elements sampled randomly from each strata. For example, class membership may be basis for distinguishing individuals in population of interest and within each category individuals are sampled randomly (EX: differentiate XYZ University Students by class membership: Freshmen, Sophomores, Juniors, Seniors) then pull random sample of students from each layer)
Multistage Cluster Sampling: Multistage cluster sampling can be useful when a sampling frame is not available, as is often the case for large populations spread across a wide geographic area or among many different organizations. In fact, if we wanted to obtain a sample from the entire U.S. population, there would be no list available. Yes, there are lists in telephone books of residents in various places who have telephones, lists of those who have registered to vote, lists of those who hold driver’s licenses, and so on. However, all these lists are incomplete: Some people do not list their phone number or do not have a telephone, some people are not registered to vote, and so on. Using incomplete lists such as these would introduce selection bias into our sample.
In such cases, the sampling procedures become a little more complex, and we usually end up working toward the sample we want through a series of steps or stages (hence the name multistage!): First, researchers extract a random sample of groups or clusters of elements that are available and then randomly sample the individual elements of interest from within these selected clusters. So what is a cluster? A cluster is a naturally occurring, mixed aggregate of elements of the population, with each element appearing in one and only one cluster. Schools could serve as clusters for sampling students, blocks could serve as clusters for sampling city residents, counties could serve as clusters for sampling the general population, and businesses could serve as clusters for sampling employees.