STRATIFIED SAMPLING:
In stratified sampling, the units included in the sample constitute roughly the same population in which they are present in the total population
Stratified sampling is also called proportional random sampling. In this sampling, the population is first subdivided into certain mutually exclusive groups or strata Such groups may be formed on the basis of geographical area / size of the household or income After stratification, a random sample of a given size is selected from each stratum of the total population This is how an attempt is being made to make the sample more representative in character Here, each of the strata is represented in the sample in relation to its importance
The following example will make this clear.
Strata income per month (Rs)
(1) |
Population number of households
(2) |
Sample (Proportionate)
(3) |
Sample (Disproportionate)
(4) |
0-500 | 5,000 | 50 | 75 |
501-1000 | 4,000 | 40 | 20 |
1001-2000 | 3,000 | 30 | 20 |
2001-3000 | 2,000 | 20 | 25 |
3001 + | 1,000 | 10 | 10 |
15,000 | 150 | 150 |
In the above example, the population consists of 15,000 households, divided into five strata on the basis of monthly income. Column (3) of the table shows the sample, i.e., number of households selected from each stratum. The sample constitutes one per cent of the population. A sample of this type, where each stratum has a uniform sampling fraction, is called a proportionate stratified sampling. If, on the contrary, the strata have variable sampling fractions, the sample is called a disproportionate stratified sample. The figures given in column (4) of the above table show a disproportionate stratified sample. It will be seen that the sampling fraction varies from one stratum to another. Thus, for example, it is 0.015 for the monthly income Rs 0-500 and 0.01 for the stratum, Rs 3001+.
It may he noted that a stratified random sample with a uniform sample fraction results in greater precision than a simple random sample. But, this is possible only when the selection within strata is made on a random basis. Further, a stratified proportionate sample is generally convenient on account of practical considerations,
There are some other considerations in favor of the stratified random sample. The researcher may be interested in the results for separate strata rather than for the entire population. A simple random sample will not show results by strata as it presents only an aggregative picture. Another consideration is that it may be administratively expedient to split the population into strata. Yet another consideration is that one can use different procedures for selecting samples from various strata. If the data are more variable in any particular strata, a larger sampling fraction should be taken in that stratum. This would result in greater overall precision
This method reduces the sampling error and it is a more accurate and representative sampling methodNaturally, it is treated as an improvement over simple random sampling. It provides information about different components of the total population Use of stratified sampling also leads to administrative conveniences In order to use a stratified sample, some information regarding the population and its strata should be available to the researcher
The process of stratified random sampling differs from simple random sampling In simple random sampling, sample items are chosen at random from the entire universe while in stratified random sampling, a separate random sample is chosen from each stratum Stratified random sampling is used in order to increase the precision of sampling estimates.
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