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Random Sample is each person of the population having equal chance of being in the sample group . This example is appropriate when the population being studied does not have great variance within it. A population of the second grade class would have less variance than the population of the entire kindergarten through sixth grade classes. Each student in second grade is academically similar with similar heights and weights, whereas the entire school would have greater variance in stature and level of education. A simple random sample is the same definition, but the first choosing is not the set of subjects studied. The sample is again randomly selected from the population and this is the set studied. This second selection further ensures the sample is truly random. (http://lc.gcumedia.com/hlt362v/the-visual-learner/random-sample.html and http://lc.gcumedia.com/hlt362v/the-visual-learner/simple-random-sample.html)

Systematic Sample is described as choosing every third or every fifth person in the population. Â This example is appropriate when the population being studied does not have several subsets. This type of sample may be used when surveying a residents of an apartment complex or a neighborhood. ( http://lc.gcumedia.com/hlt362v/the-visual-learner/systematic-sample.html )

Stratified Sampling occurs when the overall population is divided into subsets and subjects from each subset are chosen in proportion to the overall population. The subsets of the population are divided by similar traits or characteristics. If the total is 100 and there are 40 green and 60 red and the study sample is 10; there should be 4 green and 6 red to accurately represent the whole. This ensures each group within a larger group are fairly represented. This would be useful when studying a large diverse population such as employees of a hospital. Subjects would be chosen from each department to accurately represent the entire facility. ( http://lc.gcumedia.com/hlt362v/the-visual-learner/stratified-sample.html )

Cluster Sampling is a manner of dividing the population into groups (geographically related) rather than by similar traits. This type of sampling may be useful if interviewing in a large neighborhood. Subjects could be divided into blocks or by street and then one cluster is chosen to represent the whole. If surveying the city of Madison, it may be appropriate to divide the city into sections and study one section of the city. ( http://lc.gcumedia.com/hlt362v/the-visual-learner/stratified-sample.html )

Convenience Sampling occurs when researchers use the sample that is easiest to obtain. This type of sample may be used when mailing surveys to a population. Those who choose to return the survey are the subjects chosen for participation. This may also be used if the area of study is large and it would be too costly to travel to the outlying communities. If studying the residents of Alaska, traveling to outlying communities would be difficult due to lack of paved roads. Here it may be more convenient to focus on the residents who live in more developed areas. ( http://lc.gcumedia.com/hlt362v/the-visual-learner/convenience-sampling.html )

Reference:

The Visual Learner|Statistics Retrieved from: http://lc.gcumedia.com/hlt362v/the-visual-learner/the-visual-learner-v2.1.html

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