an example of using a cluster sample
Cluster sampling is the sampling method where different groups within a population are used as a sample. This is different from stratified sampling in that you will use the entire group, or cluster, as a sample rather than a randomly selected member of all groups. Examples for datasets used for cluster. analysis: • socio-economic criteria: income, education, profession, age, number of children, size of city of residence .... • psychographic criteria: interest, life style, motivation, values, involvement. • criteria linked to the buying behaviour: price range, type. The purpose of sampling is to select a set of elements from a population in such a way that the descriptions of the sample statistics accurately portray the parameters of the population. This is best achieved through use of: a. a convenience sample b. random selection c. the sampling element d. stratified sampling. Cluster sampling examples. Example 1: Assume that a study with relatively large square fixed area plots of 50 m x 50 m had been carried out, on which all tree positions were mapped. Because the individual plots were relatively large, there were only resources available to measure \(n=10\) sample plots. Despite lacking the assurance that comes from using random samples, cluster sampling is used frequently in business and other applications. The basic procedure for creating a cluster sample is to divide the full population into some sort of meaningful groups. For instance, McDonald’s® might want a sense of what the most popular item ordered on their menu is. They might create a cluster/group for each McDonald’s store. In a regional forest inventory in Northern Costa Rica, cluster plots of four sub-plots were used (as showed in figure 1), arranged at the corners of a square of 150 m side length. A sample of around 900 cluster plots was systematically laid out over the region of interest. However, cluster sampling is not as precise as simple random sampling or stratified random sampling. Therefore, data collected from this type of sampling is often skewed. The advantage of using cluster sampling is convenience and cost. And by the way, Donna won the class president race. Video: Cluster Random Samples: Definition, Selection & Examples. Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling. In a stratified sample, a population is also divided into groups, though number of groups tends to be smaller. A population could be divided by gender, age, income, and region in which they live, and comparing the result of each group may be part of the reason the stratified sample is performed. Then, we can look at all cluster plots and analyze only the observation, for example, on sub-plot 1. The corresponding data are given in Table 1. This is a correct estimation in which we use only a part of the sample plot used (the cluster plot).
Cluster sampling example: Assume that a study with relatively large square fixed area plots of 50 m x 50 m had been carried out, on which all tree positions were mapped. Because the individual plots were relatively large, there were only resources available to measure \(n=10\) sample plots.