In your own words, explain the differences among the following sampling techniques: simple random sample, stratified sample, systematic sample, cluster sample, multistage sample, and convenience sample. Describe situations in which each type might be useful.
To explain: The differences among the simple random sample, stratified sample, systematic sample, cluster sample, multistage sample, and convenience sample, and situations in which each type might be useful.
Simple random sample: Every member and set of members has an equal chance of being included in the sample. One way to achieve this is to assign each individual a number, and the randomly select numbers. This sample represents the equivalent of the entire population.
The simple random sample is often used when there is very little information available about the data population, when the data population has far too many differences to divide into various subsets, or when there is only one distinct characteristic among the data population. For example, a teacher puts students' names in a hat and chooses without looking to get a sample of students.
Stratified random sample: The population is first split into groups. The overall sample consists of some members from every group. The members from each group are chosen randomly. Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. For example, a student council surveys 100 students by getting random samples of 25 freshmen, 25 sophomores, 25 juniors, and 25 seniors.
Systematic random sample: Members of the population are put in some order. A starting point is selected at random, and every nth member is selected to be in the sample.
Systematic sampling is to be applied only if the given population is logically homogeneous, because systematic sample units are uniformly distributed over the population. The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. For example, a principal takes an alphabetized list of student names and picks a random starting point. Every 20th student is selected to take a survey.
Cluster random sample: The entire population is divided into clusters or sections and then the clusters are randomly selected. All the elements of the cluster are used for sampling. Clusters are identified using details such as age, sex, location etc. Cluster sampling is a method used extensively by government agencies and certain private research organizations...
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