As an example of Stratified random sampling?

Answer:

Simple random sampling-sampling individuals that exist in populations; individuals randomly selected from the population and placed in the sample. This method of random selection of people tends to choose the sample size, which is an objective representation of the population. However, it is not profitable, when samples of the population vary widely.

Stratified random sampling-a sampling method which involves dividing the population into smaller groups known as strata. In stratified random sampling or stratification, strata are formed based on common attributes or characteristics. Stratified random sampling is also called proportional random sampling or random quota sampling.

Stratified random sampling is a better method than simple random sampling. Stratified random sampling divides the population into subgroups, or strata, and random samples are taken in proportion to population, from each of generated layers. The members of each stratum, folded have similar features and characteristics. This method of testing is widely used and is very useful when the target population is heterogeneous. Simple random sampling should be taken from each layer. Random stratified sampling can be used, for example, in the sample students ‘ grade point Average (GPA) across the country, people who spend long hours at work, and in life all over the world.

Example

Suppose a research group wants to determine the Average score of College students in the United States, the study group challenges in collecting data from a total of 21 million students; he decides to take a random sample from the population with 4,000 students.

Suppose now that the team is considering different attributes of the participants in the sample and wonders if there are any differences in GPA and majors students. Suppose he believes that 560 students English majors, not science 1135 800 computer specialists, 1090 engineering specialties, and mathematics 415. The team wants to use a proportional stratified random sample, where the layer of the sample, a proportional random sample of the population.

Suppose a team of research demographics of College students in the United States and finds that the percentage that students of 12% in basic English 28% major in science, 24% major in computer science, 21% major in engineering, and 15% major in mathematics. Thus, five layers, created on the basis of stratified random sampling.

Then the team should confirm that stratum of the population in proportion to the layer in the sample, however, they find the proportions are not equal. Then the team must obtain permission 4000 students from the population and randomly select 480 English, science 1120, 960 computer science, engineering, 840 and 600 mathematics students. However, it has a proportional stratified random sample of College students, which provides a better representation of College students with specialties in the USA researchers can then identify specific strata, to observe the various studies of American students and observe different grade point averages. For more detailed information, please refer to Stratified random sampling.

Application

The same method used above can be applied to the polling of the election, income of different population groups, and income for a variety of jobs in the country.

For more information about the differences between simple and stratified sampling, please read what is the difference between simple random sampling and stratified random sampling?

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