# Strengths and weaknesses of stratified sampling. Probability & Non 2019-02-25

Strengths and weaknesses of stratified sampling Rating: 7,8/10 1397 reviews

## Quota sampling

This integer will correspond to the first subject. However, gains in precision may not accrue to other survey measures. The members of his sample will be individuals 5, 13, 21, 29, 37, 45, 53, 61, 69, 77, 85, 93. Disadvantages of Simple random sampling Simple random sampling suffers from the following demerits: 1. There exists a chance in simple random sampling that allows a of subjects.

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## What are the pros and cons of stratified random sampling?

Cluster Sampling First, the researcher selects groups or clusters, and then from each cluster, the researcher selects the individual subjects by either or. It is possible to divide the population in groups, with each unit from the population belonging to one group, but if the sampling requirements are extended, more groups are needed. So, we go to the stadium and assign random numbers to each person in the audience. If you are mailing out surveys or questionnaire, count on increasing your sample size by 40% to 50% to account for lost mail and uncooperative subjects. Also, since quota sampling does not need a sampling frame or spelling techniques, it is easier and quicker to perform. From there, researchers calculate each subgroup's percentage representation of the total population.

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## Stratified Sampling

The stratified random sample also improves the representation of particular strata groups within the population, as well as ensuring that these strata are not over-represented. One of the advantages of quota sampling is it helps create an accurate sample of the population when a probability sample cannot be obtained. If you were actually carrying out this research, you would most likely have had to receive permission from Student Records or another department in the university to view a list of all students studying at the university. The population is expressed as N. In the above figure, we first assigned the random numbers to each of the elements and marked the elements with highest assigned number among the elements in the same group or Row. Therefore, the stratified random sample involves dividing the population into two or more strata groups. They choose subjects because of certain characteristics.

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In general, the larger the sample, the smaller the sampling error and the better job you can do. Considering that the researcher will only have to take the sample from a number of areas or clusters, he can then select more subjects since they are more accessible. This means that we need to select 60 female students and 40 male students for our sample of 100 students. Therefore, to calculate the number of female students required in our sample, we multiply 100 by 0. Due to the representativeness of a sample obtained by simple random sampling, it is reasonable to make generalizations from the results of the sample with respect to the population.

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## Simple random sampling

The integer is typically selected so that the researcher obtains the correct sample size For example, the researcher has a population total of 100 individuals and need 12 subjects. How to reference this article: McLeod, S. This is known as proportionate stratification as opposed to disproportionate stratification, where the sample size of each of the stratum is not proportionate to the population size of the same stratum. This way, we choose the samples and ask them about their views to get an unbiased analysis of what the audience thinks in general. Dropout Risk Factors and Exemplary Programs. Opportunity Sampling Uses people from target population available at the time and willing to take part.

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## Stratified random sampling

In the case of human populations, to avoid potential bias in your sample, you will also need to try and ensure that an adequate proportion of your sample takes part in the research. It is totally free from bias and prejudice 6. On top of that, it requires proper weighting of subgroups and is less efficient for estimating population characteristics. The sample will not therefore be truly representative of the target population. Given here are the advantages of Simple random sampling. These 10,000 students are our population N. It does not take advantage of the knowledge that the researcher could have of the population.

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Participants: Students in this study will be participants that have been signified as at-risk for dropping out of school at the high school level. In this technique, each member of the population has the same probability of being selected as a subject. It is very easy to assess the sampling error in this method. The advantage to this method is that is should provide a representative sample, but the disadvantage is that it is very difficult to achieve i. For example, a researcher can use critical case sampling to determine if a phenomenon is worth investigating further. Researchers would assign every economics student at the university to one of four subpopulations: male undergraduate, female undergraduate, male graduate and female graduate.

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