Sampling technique is a way to determine the number of samples in accordance with the sample size that will be used as the data source, with regard to the nature and distribution of the population in order to obtain a representative sample.
Read also: Definition of Population and Sample
Read also: Definition of Population and Sample
There are various ways of sampling techniques based on the same assumptions, the sampling technique should optimally allows obtaining a representative sample. For that reason, there are two types of sampling technique, namely the determination of the sample with a random sampling that has a high probability to determine a representative sample and sampling technique using non-random sampling, which is less likely to produce a representative sample.
Firstly, I will share the Random Sampling Technique. Then, in the next description is the Non-random sampling Technique.
Random Sampling Technique
This technique produces a probability sample, the sample of the population whose members are given the opportunities that can be calculated to be elected as members of the sample. The samples with these methods should be adapted to the method in accordance with the characteristics of the population and research purposes. Probability sampling can be done if there is a complete list of members of the population, then the sample is determined by providing opportunities that have been calculated for each member of the population. Random sampling technique that is often used in the study are as follows:
1. Simple Random Sampling
Samples were taken in such a way so that every member of the population has an equal opportunity to be elected as members of the sample. If the sample size that we want different opportunities for each member of the population was also different. Example; population size (N) = 500 members, were sampled (n) = 50 =, the magnitude of the opportunity to elect each member of the sample is n / N = 50/500 = 0.1.
There are two ways of determining the sample by using Simple Random Sampling, namely: how to draw and a table of random numbers. This needs to be considered in determining the sample by simple random sampling are: First, the availability of the list of members population, secondly, the high level of uniformity, and thirdly, the state of the population is not too large as geographically.
2. Systematic Random Sampling
Systematic Random Sampling is a sampling technique that only the first member chosen at random, then systematically selected according to a certain pattern. Systematic random sample can be used in circumstances when the name or identification of the unit members of the population contained in a list so that the unit can be numbered. Population has an irregular pattern such as blocks in a box or a home in a way that can be numbered.
3. Stratified Random Sampling
This technique can be used when determining the sample does not only depend on the size of the population, but on the analyzed variables (expressed in hypothetical). In this case, the population is divided into several stratum with certain variables. Members of the population of the stratum were randomly selected, then summed. The total number of members of the stratum to form a new sample members called a stratified random sample. Some things to consider in stratified random sampling, namely: First, the criteria that will be used as the basis for determining the stratum should be clear. Those criteria are the variables to be studied or other variables that close relation with the variables studied. Secondly, data about the population that can be used to determine the criteria for stratification. There are two ways to determine stratified random sampling namely proportional and disproportionate.
4. Cluster Sampling
Cluster sampling is a technique of selecting a sample of clusters of small units, or clusters. The population of the cluster is a sub-population of the total population. Elements in its population is not homogeneous, the different units of the element in the strata. Each cluster has members who resemble heterogeneous population itself.
In addition, this technique is used when the list of members of the population are not available and time-consuming and high fees to obtain it. The steps are the population is divided into groups (clusters). Each group is formed to have the same characteristics with the characteristics of the population. Therefore, a study had to know the state of the population. Once the group is set, the sample can be determined randomly.
Non-Random Sampling Technique
Non-Random Sampling Technique is the other sampling technique. In this technique, the population is not given the opportunities that can be calculated to be elected as members of the sample. Sample members were selected based on certain development and should be representative. For the reason, characteristic of the population should be known as well. The determination was carried out in the absence of a random population list. Below are the kinds of non-random sampling technique.
1. Accidental Sampling
Determination of the sample is also often referred to as accidental sampling. Members of the sample is determined simply by selecting the closest respondents who first encountered at that time. Example: in research on the opinion of the audience watched a new movie, the researchers interviewed the audience immediately encountered at the time. The number of spectators who were interviewed in accordance with the number of samples needed.
2. Purposive Sampling
The samples are often called purposive sampling, in which members of the sample is determined based on certain characteristics that are considered to have a close relationship with the characteristics of the population. In this case, the researchers deliberately determine the sample members based on knowledge of population. Example: In a study conducted to determine consumer opinion cigarette X, researchers determined the number of cigarette smokers X as a sample.
3. Quota Sampling
In determining this quota sampling, the population is divided into strata. Then, each stratum is divided into two parts equally. For example: research on the opinion of TV viewers on the occasion of villages, researchers compared the opinions of those who have low education and high (these two groups into strata). For example, the number of low-educated audience is 80 million. Samples to be set is 200, then the sample in the strata of educated viewers 50 and a low of 150 people. Finally the members of the sample is determined by chance, without a list of names.
Now that's an explanation of Technique of Sampling that I have summarized from several sources. Hopefully useful and relevant to this discussion, any other questions? Thanks