There is another sampling technique that is sometimes utilized because either the researcher doesn’t know better, or it is easier to do. The four sampling techniques that were presented all have advantages and disadvantages. It uses some of the groups and all the members in each group. Cluster sampling is the other way around. In stratified sampling you use all the groups and some of the members in each group. Many people confuse stratified sampling and cluster sampling. Then record whether a tree is infected or not for every tree in that sector. Instead of having to walk all over the forest, you divide the forest up into sectors, and then randomly pick the sectors that you will travel to. You want to measure whether a tree in the forest is infected with bark beetles. Then they poll all businesses in each chosen cluster.
SIMPLE RANDOM TECHNIQUE GENERATOR
They divide the city into sections (clusters), maybe a square block for each section, and use a random number generator to pick some of the clusters. Randomly pick some clusters then poll all individuals in those clusters.Ī large city wants to poll all businesses in the city. Include convenienceĬluster sampling is where you break the population into groups called clusters. As you can see each of the sampling techniques have pluses and minuses. The problem is that if you are looking for opinions of people, and people who live in the same region may have similar opinions. If you are collecting polling data based on location, then a cluster sample that divides the population based on geographical means would be the easiest sample to conduct. The problem is that it is possible to miss a manufacturing mistake because of how this sample is taken. If your population has some order in it, then you could do a systematic sample. Also, a stratified sample has similar problems that a simple random sample has. It is best to just have one stratification. The issue with this sample type is that sometimes people subdivide the population too much. It might make sense to look at people in different age groups, or people of different ethnicities. The answers vary so much you probably couldn’t find an answer for everyone all at once. You probably don’t like the same music as your parents. It might not make sense to try to find an answer for everyone in the U.S. Now suppose you are interested in what type of music people like. However, you can get as close as you can.
There are many cases where you cannot conduct a truly random sample. This type of sample is actually hard to collect, since it is sometimes difficult to obtain a complete list of all individuals. This is where you pick the sample such that every sample has the same chance of being chosen. The simplest, and the type that is strived for is a simple random sample. There are many sampling techniques, though only four will be presented here. If this happens, it may be a good idea to collect a new sample if you have the time and money. As an example, you can take a random sample of a group of people that are equally males and females, yet by chance everyone you choose is female. Just remember to be aware that the sample may not be representative. However, there are several techniques that can result in samples that give you a semi-accurate picture of the population. That is unfortunately the limitations of sampling. None are perfect, and you are not guaranteed to collect a representative sample. If you want to test a new painkiller for adults you would want the sample to include people who are fat, skinny, old, young, healthy, not healthy, male, female, etc. When you choose a sample you want it to be as similar to the population as possible. Hopefully the sample behaves the same as the population. It might be impractical – you can’t test all batteries for their length of lifetime because there wouldn’t be any batteries left to sell. It might be too expensive in terms of time or money. \)Īs stated before, if you want to know something about a population, it is often impossible or impractical to examine the whole population.
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