Population Sampling Methodologies
Sampling Methodologies
Population:
Population is defined as including all items with the characteristic one wishes to understand. Because there is seldom enough time or money to gather information from everyone or everything in a population, the goal is to find a representative sample (or subset) of that population.
For example, a researcher might study the success rate of a new 'quit smoking' program on a sample group of 50 patients, in order to predict the effects of the program if it were made available nationwide. Here the superpopulation is "everybody in the country, given access to this treatment" - a group which does not exist, since the program isn't yet available to all.
Sampling Frame:
Sampling Frame refers to the selection of a subset of individuals/items from a population to form the sample for our survey. There are two types of sampling methods: Probability Sampling and Non-Probability Sampling.
Definition and Difference between Probability Sampling and Non-Probability Sampling
Probability methods require a sample frame.Probability methods rely on random selection in a variety of ways from the sample frame of the population. They permit the use of higher level statistical techniques and allow you to calculate the difference between your sample results and the population equivalent values so that you can confidently state that you know the population values. Non-probability methods does not have this allowance.
However non-probability samples are available even when you have no sample frame. They are generally less complicated to undertake. They minimise the preparation costs of a survey, and are used when you are actually unsure of the population of interest.
Types of Probability Sampling:
1.Simple random sampling:
In a simple random sampling of a particular size, all subsets of the frame are given an equal probability. Each element of the frame has an equal probability of selection: the frame is not subdivided. Furthermore, any given
Population:
Population is defined as including all items with the characteristic one wishes to understand. Because there is seldom enough time or money to gather information from everyone or everything in a population, the goal is to find a representative sample (or subset) of that population.
For example, a researcher might study the success rate of a new 'quit smoking' program on a sample group of 50 patients, in order to predict the effects of the program if it were made available nationwide. Here the superpopulation is "everybody in the country, given access to this treatment" - a group which does not exist, since the program isn't yet available to all.
Sampling Frame:
Sampling Frame refers to the selection of a subset of individuals/items from a population to form the sample for our survey. There are two types of sampling methods: Probability Sampling and Non-Probability Sampling.
Definition and Difference between Probability Sampling and Non-Probability Sampling
Probability methods require a sample frame.Probability methods rely on random selection in a variety of ways from the sample frame of the population. They permit the use of higher level statistical techniques and allow you to calculate the difference between your sample results and the population equivalent values so that you can confidently state that you know the population values. Non-probability methods does not have this allowance.
However non-probability samples are available even when you have no sample frame. They are generally less complicated to undertake. They minimise the preparation costs of a survey, and are used when you are actually unsure of the population of interest.
Types of Probability Sampling:
1.Simple random sampling:
In a simple random sampling of a particular size, all subsets of the frame are given an equal probability. Each element of the frame has an equal probability of selection: the frame is not subdivided. Furthermore, any given