Sampling Vs. Stratified Random Sampling

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Simple Random Sampling vs. Stratified Random Sampling Sampling involves selecting a subset of elements from the population. In this case, Stratified Random Sampling, and Simple Random Sampling plans are compared as data collection methods for a sample that a researcher would consider using for a business survey for a marketing/advertising campaign. Simple Random Sampling is a sampling procedure whereby the researcher defines the target population and then selects a sampling frame from the population. He then selects individual elements within the sampling frame with each element having an equal probability of being chosen. Stratified Random Sampling is similar to Simple Random Sampling in that it is also a probability sampling technique. However, there exists a major difference in that, with Stratified random sampling, the researcher divides the population into different groups called strata. He then selects individual elements from each stratum at random. The same probability of being selected applies for each of the elements in the population. As such, stratified sampling requires that the strata should not overlap in order to avoid some elements in the population having a higher chance of being selected than others. The most common used strata include; age, socioeconomic status, religion and nationality. Strengths and weaknesses The main strength of Simple Random Sampling that it is most likely to produce representative samples and permits the use of inferential
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