A sample is part of a population, and researchers use samples to collect data and information about a variable or variables from the larger population. To obtain samples that are unbiased there are mainly four different sampling techniques or methods, random sampling, stratified sampling, cluster sampling, and systematic sampling. A random sample is a sample where every member of the population has an equal chance of being selected.
There are a few different ways to do this, the researcher could number each member of the population, to keep it simple say a population of 90 members, he or she could then place numbered cards 1 through 90 in a hat or bowl or mixer, and select as many cards as needed to complete the sample. Or they can use a calculator or computer to generate random numbers, or they could use a random number table like this one. A stratified sample is where a researcher will divide the population into subgroups to have members from each segment of the population, and a random sample is derived from each subgroup.
For example let's say you wanted to know how much money people saved on a yearly basis, you could have subgroups of people in their 20s, in their 30s, in their 40s, and in their 50s. You would then take a random sample for each of these groups. A cluster sample is obtained by dividing the population into sections or clusters, then randomly selecting one or more of the clusters and using all of its members, as members of the sample.
This is often used when the population is large or there's a large geographic area. For instance let's say you wanted to survey small business owners in a very populated city, it would be costly and time-consuming to survey every single small business owner. So you could create a cluster sample, using zip codes and maybe survey two or three of the thirteen different possible zip codes.
Cluster samples can be efficient and cost effective, however there are times when the cluster does not represent the population. A little note, the main difference between cluster sampling and stratified sampling is that subgroups in the stratified sample have similar characteristics and the subgroups or clusters in the cluster sample are intended to vary in characteristics. A systematic sample is where a researcher will assign a counting number to have of the population, then select a random number, then select members for the sample at regular intervals, from the starting random number that was selected.
For example let's say you wanted to know how much time people living in a singles only apartment complex spent watching Netflix on a weekly basis. You would want to get, say, a sample of 50 members. If there were a thousand units in the complex you could number the units one to a thousand and generate a random starting number, say 234, since you want a sample of 50, you could divide 1,000, the number of the population, by 50, to get 20, which would be your interval number.
So, 234 would be selected for the sample, then 254, which is 234 plus 20, then 274, which is 254 for plus 20, and so on until you had your 50 members selected for your sample. This sampling method is easy to use if the population can be easily numbered. Another note, even when using the best sampling methods a sampling error, which is the difference between the results of a sample and a population can occur, and there is one other sampling method called a convenience sample, where a researcher develops a sample from members of the population that are easy to get or convenient.
Many times these samples lead to biased results. Alright my friends, I have more videos right there for you, till next time, I am outta here.
