Sampling

Probability Sampling

Probability sampling strategies include simple random sampling as well as more complex sampling designs, such as stratified sampling and cluster sampling (and its variants such as probability proportional to size sampling). In simple random sampling, a random subset (n) of the target population (N) is selected, with each member of N having an equal probability of selection. For example, imagine if one wished to study how life satisfaction changes across the life span among adults (i.e., those aged 18 and older) who reside in the state of Michigan, which according to the most recent U.S. Census has an adult population of roughly 7.5 million. In order for a sample of the Michigan population of adults to qualify as a simple random sample, each of the 7.5 million adults currently residing in the state would have to have the exact same probability of selection (i.e., 1 in 7.5 million or .000013%). Of course, this would be very difficult to achieve because it would entail compiling a complete and accurate list of names for these 7.5 million adults, taking a random sample from that list, and then tracking down all those randomly selected for the purposes of data collection. Consequently, because gathering a simple random sample is often not feasible, other versions of probability sampling, which typically are referred to as complex random samples, are more common. One type of complex random sampling is stratified sampling, which entails dividing the target population into separate groups called strata (such as ethnic groups), and then a probability sample (often a simple random sample) is drawn from each stratum. Cluster sampling is another type of complex random sampling, which entails dividing the target population into separate geographic groups called clusters (such as schools, neighborhoods, or households). Next, a simple random sample of clusters is selected from the population, and data collection is limited to those who fall within these randomly selected clusters. Within each selected cluster, data collection can be probability based (i.e., based on a simple random sample or a stratified design) or complete (i.e., every individual within a given cluster is eligible to participate in the study). Although these population-based probability sampling strategies differ from one another in important ways, they all, when carried out properly, should yield an unbiased sample that is representative of the target population, such that the demographic characteristics of the sample faithfully reflect the demographic characteristics of the target population.

...