Types Of Samples

Types Of Samples

1. Simple Random Sample

   • Every subset of a specified size n from the population has an equal chance of being selected.
     • 1. The names of 25 employees being chosen out of a hat from a company of 250 employees. 2. For larger populations, a manual lottery method can be quite onerous. Selecting a random sample from a large population usually requires a computer-generated process, by which the same methodology as the lottery method is used, only the number assignments and subsequent selections are performed by computers, not humans.

2. Stratified Random Sample

   • Divide the population into groups called strata and then take a sample from each stratum.
     • 1. Age, socioeconomic divisions, nationality, religion, educational achievements and other such classifications fall under stratified random sampling. 2. Let’s consider a situation where a research team is seeking opinions about religion amongst various age groups. Instead of collecting feedback from 326,044,985 U.S citizens, random samples of around 10000 can be selected for research. These 10000 citizens can be divided into strata according to age,i.e, groups of 18-29, 30-39, 40-49, 50-59, and 60 and above. Each stratum will have distinct members and number of members.

3. Cluster Sampling

   • The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed.
     • A researcher wants to survey academic performance of high school students in Spain. He can divide the entire population (population of Spain) into different clusters (cities). Then he selects a number of clusters depending on his research through simple or systematic random sampling. Then, from the selected clusters (randomly selected cities) the researcher can either include all the high school students as subjects or he can select a number of subjects from each cluster through simple or systematic random sampling.

4. Systematic Sample

   • Every certain “#” th member (for example: every 10th people) is selected from a list of all population member.
     • For example, if you wanted to select a random group of 1,000 people from a population of 50,000 using systematic sampling, all the potential participants must be placed in a list and a starting point would be selected. Once the list is formed, every 50th person on the list (starting the count at the selected starting point) would be chosen as a participant, since 50,000/1,000 = 50. For example, if the selected starting point was 20, the 70th person on the list would be chosen followed by the 120th, and so on. Once the end of the list was reached and if additional participants are required, the count loops to the beginning of the list to finish the count.