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#Analyzing cluster search prodiscover basic generator

The number of clusters identified from data by algorithm is represented by ‘K’ in K-means. It is also called flat clustering algorithm. It assumes that the number of clusters are already known. K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid.

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The resulting sample is much smaller and therefore easier to collect data from.

From within those classes, you randomly select a sample of students.

From each school, you randomly select a sample of seventh-grade classes.

Example: Multistage samplingInstead of collecting data from every seventh-grader in the selected schools, you narrow down your sample in two additional stages: You should use this method when it is infeasible or too expensive to test the entire cluster. You can also continue this procedure, taking progressively smaller and smaller random samples, which is usually called multistage sampling. You can then collect data from each of these individual units – this is known as double-stage sampling. In multistage cluster sampling, rather than collect data from every single unit in the selected clusters, you randomly select individual units from within the cluster to use as your sample. You then conduct your study and collect data from every unit in the selected clusters.ĭata collectionYou test the reading levels of every seventh-grader in the schools that were randomly selected for your sample. You then use a sample size calculator to estimate the required sample size. This in turn is based on the estimated size of the entire seventh-grade population, your desired confidence interval and confidence level, and your best guess of the standard deviation (a measure of how spread apart the values in a population are) of the reading levels of the seventh-graders. You choose the number of clusters based on how large you want your sample size to be.

#Analyzing cluster search prodiscover basic generator

SampleYou assign a number to each school and use a random number generator to select a random sample. If each cluster is itself a mini-representation of the larger population, randomly selecting and sampling from the clusters allows you to imitate simple random sampling, which in turn supports the validity of your results.Ĭonversely, if the clusters are not representative, then random sampling will allow you to gather data on a diverse array of clusters, which should still provide you with an overview of the population as a whole. Step 3: Randomly select clusters to use as your sample There is no overlap because each student attends only one school. To cover the whole population, you need to include every school in the city. You should be aware of this when performing your study, as it might affect its validity.ĬlustersYou cluster the seventh-graders by the school they attend. However, in practice, clusters often do not perfectly represent the population’s characteristics, which is why this method provides less statistical certainty than simple random sampling.īecause clusters are usually naturally occurring groups, such as schools, cities, or households, they are often more homogenous than the population as a whole. Ideally, each cluster should be a mini-representation of the entire population. the same people or units do not appear in more than one cluster). There not be any overlap between clusters (i.e.Taken together, the clusters should cover the entire population.Each cluster should have a similar distribution of characteristics as the distribution of the population as a whole.

You want every potential characteristic of the entire population to be represented in each cluster.

Each cluster’s population should be as diverse as possible.

Ideally, you would like for your clusters to meet the following criteria: The quality of your clusters and how well they represent the larger population determines the validity of your results. This is the most important part of the process. PopulationIn your reading program study, your population is all the seventh-graders in your city. Step 1: Define your populationĪs with other forms of sampling, you must first begin by clearly defining the population you wish to study. You thus decide to use the cluster sampling method. However, you can easily obtain a list of all schools and collect data from a subset of these.

It would be very difficult to obtain a list of all seventh-graders and collect data from a random sample spread across the city. Research exampleYou are interested in the average reading level of all the seventh-graders in your city. The simplest form of cluster sampling is single-stage cluster sampling. Frequently asked questions about cluster sampling.Probability vs non-probability sampling.