Sampling methods
Sampling methodologies
Sampling
It may be defined as a process of selecting units that may be people, organizations etc, from a larger whole i.e. from a population of interest, so that by studying the sample we may come up with general characteristics of the entire population under consideration. Types of sampling methods:
Probability sampling
Probability sampling is a type of sampling that includes random selection. And in order to achieve random selection, it must be made sure that different units of population have equal probability of being chosen.
Some relevant terms:
N = the number of cases in the sampling frame n = the number of cases in the sample f = n/N = the sampling fraction
I] Simple Random Sampling
It is the simplest type of probability sampling, wherein the probability of an element getting selected is directly proportional to its frequency. It is equivalent to say that every element has the same probability of getting chosen if they have the same frequency. For example in a random number generator each element has the same frequency and hence the same probability i.e. f=n/N.
It may be the simplest method but it is not considered as the statistically efficient.
II] Systematic Random sampling:
In systematic sampling, we first arrange the elements in certain systematic order. The selection of an element for sampling is done at regular interval, say k. the main difference between simple random and this method is that the probability of the selection of elements may not be same for the elements of same frequency.
III] Stratified Sampling: The first step of this method includes dividing the entire population into homogenous subgroups. The sampling process is then applied on it based on certain criteria. The major advantage of this method is that, it represents small subgroups rather than the entire population. Moreover, it helps to apply different sampling criteria on different subgroups.
IV] Cluster
Sampling
It may be defined as a process of selecting units that may be people, organizations etc, from a larger whole i.e. from a population of interest, so that by studying the sample we may come up with general characteristics of the entire population under consideration. Types of sampling methods:
Probability sampling
Probability sampling is a type of sampling that includes random selection. And in order to achieve random selection, it must be made sure that different units of population have equal probability of being chosen.
Some relevant terms:
N = the number of cases in the sampling frame n = the number of cases in the sample f = n/N = the sampling fraction
I] Simple Random Sampling
It is the simplest type of probability sampling, wherein the probability of an element getting selected is directly proportional to its frequency. It is equivalent to say that every element has the same probability of getting chosen if they have the same frequency. For example in a random number generator each element has the same frequency and hence the same probability i.e. f=n/N.
It may be the simplest method but it is not considered as the statistically efficient.
II] Systematic Random sampling:
In systematic sampling, we first arrange the elements in certain systematic order. The selection of an element for sampling is done at regular interval, say k. the main difference between simple random and this method is that the probability of the selection of elements may not be same for the elements of same frequency.
III] Stratified Sampling: The first step of this method includes dividing the entire population into homogenous subgroups. The sampling process is then applied on it based on certain criteria. The major advantage of this method is that, it represents small subgroups rather than the entire population. Moreover, it helps to apply different sampling criteria on different subgroups.
IV] Cluster
References: http://www.wyzant.com/help/math/statistics_and_probability/introduction/sampling http://www.socialresearchmethods.net/kb/sampprob.php