paratusojitos0yx

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2022-11-22

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Explain why you are more likely to obtain a representative sample with proportionate stratified random sampling than with either stratified random sampling or simple random sampling.

Explain why you are more likely to obtain a representative sample with proportionate stratified random sampling than with either stratified random sampling or simple random sampling.

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Nkgopotsev1g

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2022-11-23Added 15 answers

A sample is any number of individuals taken from the population for the purpose of research analysis. A sample is considered to be the true representation of a population. There are different methods of collecting a sample. The three methods are; Random Sampling, Probability Sampling and Non-Probability Sampling. The whole idea behind sampling is to extract a smaller amount of individuals from a worldwide population while still representing the population. The population of our country is over 3 million. We cannot include everybody in the experiment, thus we try to get a sample which is the true representation for the over 3 million population.

However, you are more likely to obtain a representative sample with proportionate stratified random sampling rather than with either stratified random sampling or simple random sampling. The major reason being, proportionate stratified sampling includes random equal number of individuals from each stratum of the population. For instance, the experimenter wants to conduct a research to evaluate level of frustration between working professionals in four corporate sectors (Advertising and Marketing, Banking and Finance, Information Technology and Mass Media and communication). Stratified random sampling would include equal number of participants from each of the sectors. The experimenter would choose exact 50 individuals from each sector, 25 males and 25 females. Had he chosen to implement disproportionate stratified random sampling method, the participants would have not been in proportion. It could include 60, 70, 50 and 20 from each of the sectors. The sample size would have been the same, however, the representing sample will not be obtained. So is the problem with simple random sampling. Through lottery or fishbowl method every individual has equal, probable chances of being selected in the sample. However, for the above study, this method is not accurate because it is not the true representation of the population.

Thus, you are more likely to obtain a representative sample with proportionate stratified random sampling.

However, you are more likely to obtain a representative sample with proportionate stratified random sampling rather than with either stratified random sampling or simple random sampling. The major reason being, proportionate stratified sampling includes random equal number of individuals from each stratum of the population. For instance, the experimenter wants to conduct a research to evaluate level of frustration between working professionals in four corporate sectors (Advertising and Marketing, Banking and Finance, Information Technology and Mass Media and communication). Stratified random sampling would include equal number of participants from each of the sectors. The experimenter would choose exact 50 individuals from each sector, 25 males and 25 females. Had he chosen to implement disproportionate stratified random sampling method, the participants would have not been in proportion. It could include 60, 70, 50 and 20 from each of the sectors. The sample size would have been the same, however, the representing sample will not be obtained. So is the problem with simple random sampling. Through lottery or fishbowl method every individual has equal, probable chances of being selected in the sample. However, for the above study, this method is not accurate because it is not the true representation of the population.

Thus, you are more likely to obtain a representative sample with proportionate stratified random sampling.

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