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In some situations stratified random sampling may be more appropriate. You may have a population which can be divided up into a number of subsets (strata) such that the difference between units in different strata is much greater than the difference between units within each stratum. A probability sample may not have enough units from some of the smaller strata. A stratified random sample will ensure that each stratum is represented proportionally.
In other situations, cluster sampling may be more appropriate. Suppose you wish to visit a sample 1% of all schools in the country. If you were to choose the schools by probability sampling they would be all over the country and you would require a huge amount of time and money to visit them all. What you could do, instead, is to divide up the country into 1000 regions. Select 10 of these regions (1%) and then visit every school in the selected regions. Far less running around!
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Differences between probability samplingand non-probability sampling?
the difference is just that non-probability sampling does not involve random selection, but probability sampling does.
Pros and Cons of non-probability sampling?
Pros and Cons of a non-probability sampling
What is sampling examine well known methods of probability and nonprobability sampling in statistic?
Non probability sampling and probability sampling are different because probability sampling uses random samples. Non probability sampling aren't random, but can still be representative of the population as a whole if done correctly.
What difference between Statistical Sampling and non statistical sampling?
an approach to sampling that has the characteristics of being randomly selected and the use of probability theory to evaluate sample results. Whereas non-statistical sampling is therefore any sampling approach that does not have both of the characteristicss of statistical sampling. I hope this will help....
What are the different non-probability sampling techniques?
The related web sites give a good idea of the types of non-random sampling. These include snowball, convenience, quota, self-selection, diversity, expert, and others. Non-randon sampling is usually done because it is less expensive, easier, and quicker than random sampling.
