When a study’s population of interest is massive, the standard sampling procedure, random sampling, becomes infeasible. In such a case, researchers must use other forms of sampling. One such form is multi-stage sampling. Multi-stage sampling divides the population into distinct groups in a way that makes the between-group variance low and the within-group variance high. This sampling procedure has its pros and cons.
The main purpose of the creation and present-day use of multi-stage sampling is to avoid the problems of randomly sampling from a population that is larger than the researcher’s resources can handle. Multi-stage sampling gives researchers with limited funds and time a method to sample from such populations. This sampling procedure in essence is a way to reduce the population by cutting it up into smaller groups, which then can be the subject of random sampling. As long as the groups have low between-group variance, this form of sampling is a legitimate way to simplify the population.
The multi-stage form of sampling is flexible in many senses. First, it allows researchers to employ random sampling or cluster sampling after the determination of groups. Second, researchers can employ multi-stage sampling indefinitely to break down groups and subgroups into smaller groups until the researcher reaches the desired type or size of groups. Last, there are no restrictions on how researchers divide the population into groups/ This allows a large number of possibilities for methods of convenience, the maximization or minimization of variance or interpretability.
The flexibility of multi-stage sampling is a double-edged sword. Because of the lack of restrictions on the decision processes involved in choosing groups, multi-stage sampling has a level of subjectivity. Thus, there will always be questions as to whether the chosen groups were optimal. Researchers must find a way to justify their choices when presenting the study’s findings.
Disadvantage: Lost Data
Due to the fact that multi-stage sampling cuts out portions of the population from the study, the study’s findings can never be 100 percent representative of the population. Even though the theory of multi-stage sampling is to focus on the within-group variance and de-emphasize the between-group variance (which should be minimized), there is no way to know if the demographics cut from the study could have provided any useful information to researchers. Data gets lost in the sense that not everyone is counted. But even a total population census is imperfect because addresses in remote areas may be missing and transient individuals may be difficult to identify and interview.