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# Video – Sampling Methods

Students will learn the difference of probability and non-probability sampling and be able to distinguish between the different key sampling technique
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Sampling methods. This video will help you to understand the various techniques of sampling, and the type of sampling. Mainly, there are two types of sampling, probability samples and non-probability samples. Probability samples, each member of the population has a known non-zero probability of being selected. Methods include random sampling, systematic sampling, stratified sampling, and cluster sampling. Whereas non-probability samples, members are selected from the population in some nonrandom manner. Methods include convenience sampling, judgment sampling, quota sampling, and snowball sampling. Random sampling. Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected.
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When there are very large population, it is often difficult to identify every member of the population, so the pool of available subject becomes biased. One can generate a random number by using softwares. Random sampling, as you can see from the diagram, no specific techniques or approach applied. Whereas, the sample taken from the population, by giving equal chance of being selected within that population to individual. Systematic sampling. Systematic sampling is often used instead of the random sampling. It is also called an Nth name, selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members.
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As long as the list does not contain any hidden order, this sampling method is as good as random sampling method. It’s only advantage over the random sampling technique, it is simplicity, and possibly cost effectiveness as well. Systematic sample, as you can see from the diagram, containing all the probability samples, the list of it. The diagram of houses shows the yellow house which is being selected as a sample using systematic sample technique, giving the name Nth order which is a second house in the street. The sample has been taken from every second house in the street, that is why the name is called systematic sampling.
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A stratified sampling is commonly used probability method that is superior to random sampling because it is reduce sampling error. A stratum is a subset of the population that share at least one common characteristics, such as male and female. Identify relevant stratums and their actual representation in the population is one of the key for a stratified sampling. Random sampling is then used to select a sufficient number of subjects from each stratum. A stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums. A stratified sampling shows the proportionality towards the whole entire population.
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Therefore, a stratified sample can be collected from each group of the population in order to apply stratified sampling techniques on it. Cluster sampling. Cluster sample, a probability sample in which each sampling unit is a collection of elements. This method is effective under the following conditions, a good sampling frame is not available, or if it is available, then it is available on high cost, while a frame listing cluster is easily obtained. The cost of obtaining observation increases as the distance separating the element increases. For example, city blocks in terms of collecting information, political or geographical point of view, housing units, college students, hospital, illnesses, automobiles, set of four tires looking for various varieties.
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Cluster sampling is a sampling when you select in the form of group. As you can see in the diagram, population consists, for example, four group one, 1, 2, 3, 4. You may wish to collect a sample Group 1, because that may wishes all the goals of your research, or Group 4. Non-probability sampling, convenience sample. Convenience sampling is used in exploratory research, where the researcher is interested in getting an inexpensive approximation. The sample is selected because they are convenient. It is a non-probability method. Often used during preliminary research efforts to get an estimate without incurring the cost or time required to select a random variables. Judgment sampling. Judgment sampling is a common non-probability method.
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The sample is selected based upon judgment, an extension of convenience sampling. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population. A snowball sampling. Snowball sampling is a special non-probability method used when the desired sample characteristics is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. This technique relies on referrals from initial subjects to generate additional subjects. It lower search costs. However, it introduces biasness because the technique itself reduces the likelihood that the sample will represent a good cross section from the population. Lastly, quota sampling. Quota sampling is the non-probability equivalent of the stratified sampling.
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First, identify the stratums and their proportions as they are represented in the population. Then, convenience or judgment sampling is used to select the required number of subjects from each stratum.