Strengths and weaknesses of stratified sampling
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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.

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.

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.

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.

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.

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.

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.

If a pattern in the population exists and it coincides with the interval set by the researcher, randomness of the sampling technique is compromised. Researchers obtain volunteer samples by advertising on posters or in newspapers. Probability Sampling uses lesser reliance over the human judgment which makes the overall process free from over biasness. For example, in studying the problems of middle class working people in a state, the first stage will be to pick up a few districts in the state. It takes lesser time to complete.

Instead of an entire country when using simple random sampling, the researcher can allocate his limited resources to the few randomly selected clusters or areas when using cluster samples. However, in line with proportionate stratification, the total number of male and female students included in our sampling frame would only be equal if 5,000 students from the university were male and the other 5,000 students were female. No student could fit into both categories ignoring transgender issues. It also means that it is not possible to make statistical inferences from the sample to the population. This type of discrepancy is known as sampling error.