Sampling
The general idea behind sampling is the extrapolation from the sample to the population. It must be done in such a manner that the sample that is being drawn from the population should represent the population as a whole. The method of choosing the type of sampling is called design.
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An appropriate type of sampling involves probability. Sampling that is done with the help of probability methods is called probability sampling. Biased results or estimates are serious problems, and the researcher can get rid of these with the help of probability.
In order to conduct sampling by means of probability, it is important to identify the population of interest. The next step is then to create the sampling frame.
There is another type that is more flexible and easy to understand to the person not familiar with statistics. This type is nothing but simple random sampling. For instance, in order to conduct simple random sampling of 100 units of an item, the researcher chooses one unit at random, and then the second unit, (and so on) until the 100th unit has been chosen by means of simple random sampling. In each step, every unit has a similar chance of getting selected.
This type is generally practically feasible in cases where the population consists of business records. The consequence would not get affected even when the population is of a larger size.
There are two kinds of errors in sampling, namely random error and systematic error.
Sampling error generally occurs in cases where the researcher gets very few units of a desirable sample from the population. The obvious consequence is generally quantified by utilizing the standard error or simply ‘SE.’
In the case of sampling involving probability, the SE can be estimated by using the sample design and the sample data. As the size of the sample increases, then the SE gets decreased. So, if the population on which the sampling is being carried out is relatively homogeneous, then the SE will be small.
In cases of the sampling involving cluster, there is generally a larger SE. However it should be noted that sampling that involves clusters are generally cost effective.
The non-sampling error is generally more serious as the non-sampling errors are usually harder to quantify and therefore draw less attention. This problem cannot be controlled by increasing the size of the sample. The non-sampling error can be categorized into three categories: selection bias, non response bias, and response bias.
The first category of non-sampling error is selection bias and it is a systematic tendency to exclude one kind of unit from the sample. In cases of sampling that involve probability, this type of bias is generally minimal.
The second category of bias for non-sampling errors usually occurs in those cases when the respondents do not respond to sensitive questions. In order to minimize this type of bias of the non-sampling error, the response rate should be kept high.
The third category of the bias of the non-sampling error occurs in cases when the respondent does not answer the question honestly.