Question

 

In probability sampling, every item has a chance of being selected. There are 4 types of probability sampling. Can you name them and provide examples.

Answer 1

4 TYPES OF PROBABILITY SAMPLING:

1. Simple random sampling is a completely random method of selecting subjects. These can include assigning numbers to all subjects and then using a random number generator to choose random numbers.

Example : - An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample israndom because each employee has an equal chance of being chosen.

Another example can be the method of lottery. It is the most primitive and mechanical example of random sampling. In this method you will have to number each member of population in a consequent manner, writing numbers in separate pieces of paper. These pieces of papers are to be folded and mixed into a box. Lastly, samples are to be taken randomly from the box by choosing folded pieces of papers in a random manner.

2. Stratified Random Sampling involves splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.

Example :- Assume that we need to estimate average number of votes for each candidate in an election. Assume that country has 3 towns: Town A has 1 million factory workers, Town B has 2 million office workers and Town C has 3 million retirees. We can choose to get a random sample of size 60 over entire population but there is some chance that the random sample turns out to be not well balanced across these towns and hence is biased causing a significant error in estimation. Instead if we choose to take a random sample of 10, 20 and 30 from Town A, B and C respectively then we can produce a smaller error in estimation for the same total size of sample.

3. Systematic Sampling means that you choose every “nth” participant from a complete list. For example, you could choose every 10th person listed.

Example :-  Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample.

Another example can be, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.

4. Cluster Random Sampling In cluster sampling, instead of selecting all the subjects from the entire population right off, the researcher takes several steps in gathering his sample population.First, the researcher selects groups or clusters, and then from each cluster, the researcher selects the individual subjects by either simple random or systematic random sampling.

Example:-

Suppose that a researcher wants to survey academic performance of high school students in Spain.

1. He can divide the entire population (population of Spain) into different clusters (cities).
2. Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.
3. 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.