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In order to perform a study and gain statistical data, we need to start with a target population and then get a sample of that target population. An example of a sample would be to take a survey of 5,000 mothers with children under the age of one year old in Marion county. This would be a sample of all mothers in Marion county. In order to get the most accurate data from the study there are several types of sampling techniques used. There is cluster sampling, random sampling, stratified random sampling, convenience sampling and systematic sampling ("The visual learner," n.d.).

Cluster sampling refers to the researcher separating the group into smaller groups called clusters. Then a simple random selection of clusters is picked from the population. Then the researcher can conduct his analysis of data from the sampled clusters. The most common form of cluster sampling used is a geographical cluster. An example of this is if a researcher wants to study the academic performance of high school students in China. The researcher can divide the entire population of China into individual clusters such as cities. Then the researcher can select a number of clusters depending on the research done through simple or systematic random sampling. Then, from the randomly selected cities the researcher can either include all the high school students as subjects or he can select a number of subjects from each cluster through simple or systematic random sampling.

Random sampling is where every member of the study population has an equal opportunity to be chosen to be in a study. Random sampling involves identifying each person in a target population and then randomly selecting a sample from that population. This allows every person of the study population to be represented without showing bias and minimizes errors. An example of this is when a there are 30 employees names drawn out of a hat from a company of 3,000 employees. Each person has equal opportunity to be drawn out of the hat.

Stratified random sampling is the division of a population into smaller groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics. When running analysis or research on a group of individuals with similar characteristics, a researcher may find that the population size is too large to run a research on. To save time and money, an analyst may take on a more feasible approach by selecting a small group from the population. The small group used is referred to as a sample size, which is a subset of the population that is used to represent the population. There are a number of ways a sample may be selected from a population, one of which is the stratified random sampling method. An example is when an academic researcher would like to know the number of BSN students that got a job within three months of graduation in 2014-2015. It is noted that there were almost 216,000 BSN graduates for the year. It might be better to just take a simple random sample of 50,000 grads and run a survey or divide the population into strata and take a random sample from the strata. To do this, the researcher could create population groups based on gender, age range, race, country of nationality, and career background. A random sample would then be taken from each stratum in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample.

A convenience sample is a group that is made up of individuals or elements that are easy to reach or obtain (Rumsey, 2010, p. 139). This is also referred to as a grab sample because we grab individuals from the population for our sample. An example of this is when a poll is being taken at a local mall or market. This type of sample is very easy to obtain but the lack of effort for the samples and the sample itself is ultimately worthless for believable statistical purposes.

Finally, there is systematic sampling is a type of probability sampling method where sample members from a larger population are selected according to a random starting point and a fixed, periodic sampling interval. This interval is calculated by dividing the population size by the desired sample size. Systematic sampling is still thought of as being random if the periodic interval is determined beforehand and the starting point is random. An example of this is if you want to sample 8 houses from a street of 120 houses. Then every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116. But then if every 15th house was a "corner house" then this corner pattern could destroy the randomness of the sample.

References

Rumsey, D. J. (2010). Statistic essentials for dummies (1st ed.). Retrieved on October 2, 2018 from Http://dummies.com/education/math/statistics

The Visual Learner. (n.d.). Statistic Terms. Retrieved October 2, 2018, from https://lc.gcumedia.com/hlt362v/the-visual-learner…