In non proportionate sample equal representation is

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Unformatted text preview: ccording to its size. In non-proportionate sample, equal representation is given to all the sub-strata regardless of their existence in the population. Example 2: A sample of 50 students is to be drawn from a population consisting of 500 students belonging to two institutions A and B. The number of students in the institution A is 200 and the institution B is 300. How will you draw the sample using proportional allocation? Solution: There are two strata in this case with sizes N1 = 200 and N2 = 300 and the total population N = N1 + N2 = 500 The sample size is 50. If n1 and n2 are the sample sizes, n1 = n 50 ×200=20 ×N1 = N 500 n2 = n 50 ×300=30 ×N2 = N 500 The sample sizes are 20 from A and 30 from B. Then the units from each institution are to be selected by simple random sampling. Merits and limitations of stratified sampling: Merits: 1. It is more representative. 2. It ensures greater accuracy. 20 3. It is easy to administer as the universe is sub - divided. 4. Greater geographical concentration reduces time and expenses. 5. When the original population is badly skewed, this method is appropriate. 6. For non – homogeneous population, it may field good results. Limitations: 1. To divide the population into homogeneous strata, it requires more money, time and statistical experience which is a difficult one. 2. Improper stratification leads to bias, if the different strata overlap such a sample will not be a representative one. 2.5.3 Systematic Sampling: This method is widely employed because of its ease and convenience. A frequently used method of sampling when a complete list of the population is available is systematic sampling. It is also called Quasi-random sampling. Selection procedure: The whole sample selection is based on just a random start . The first unit is selected with the help of random numbers and the rest get selected automatically according to some pre designed pattern is known as systematic sampling. With systematic random sampling every Kth element in the frame is selected for the sample, with the starting point among the first K ele...
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