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9+ Sampling Techniques in Research

The sampling technique is a method used to select samples from a population, so that the sample can represent the population. Sampling is used to save time, energy and money.

Although not examining individual elements in the population, sampling with the right method can give results that are close to the actual results.

One application the sampling technique that you often see is when quick count presidential elections, where the elected president can be predicted within a few hours even though the population of Indonesia is more than 250 people. Well, the prediction was produced not by counting the choices of all populations but only selected samples.

Sampling Techniques

In general, the sampling technique was divided into two major classes, namely probability sampling and non-probability sampling.

The difference between the two classes lies in the selection used, where for probablity sampling the sample is chosen randomly while for non-probability sampling is not random. If done randomly, then every subject in the population has the same opportunity to be chosen.

A. Probability Sampling

1. Simple Random Sampling

 Simple random sampling

Simple random sampling is the most basic sampling technique, where each member of the population has the same probability to be chosen . As the name implies, the method of sampling is done randomly and does not pay attention to groups or strata that exist in the population.

Although in theory it is quite easy to understand, but in practice this technique is difficult to apply, so this technique is rarely used . The advantages of this sampling technique include errors that can be calculated and can reduce bias in sample selection.

One way of sampling with simple random sampling technique is numbering. For example the population size of 1000 individuals. We can label 0 to 999 for each individual. Then use the random number table to determine the sample. If the first three numbers selected from the random table are 008, then the individual selected is labeled 8.

2. Stratified Sampling

 Stratified Sampling

The sampling technique is carried out by dividing populations into groups or strata based on the equations they have in each group. Or in other words, the elements in one group have homogeneous properties while between groups will be heterogeneous. The method of sampling is by randomly selecting elements from each group.

Following an example of this sampling technique, suppose there is a study of whether women in a menstrual area regularly or not. We can divide women in the region into several age groups, for example ages 11-15 years, ages 16-20 years, ages 21-24 years, and so on. Then from each age group random individuals will be sampled. And don't forget that the number of samples taken from each group must have the same proportion to the number of each group.

3. Clustered Sampling

 Clustered Sampling

In the clustered sampling technique, the entire population must be divided into several clusters, for example based on individual locations. This cluster will act as a sampling unit. From these clusters, several clusters will be chosen randomly. All individuals from the selected clusters will be used as samples.

Clustered sampling can be done by two methods, namely single stage cluster sampling and two stage cluster ] sampling. Single stage cluster sampling by selecting clusters randomly and using all elements of these clusters as research samples.

For two stage cluster sampling sample selection is continued up to two stages, first selecting random clusters, then choosing elements from randomly chosen clusters. These elements will serve as research samples.

Clustered sampling is considered more efficient than simple random sampling when it involves large areas. Because, it will be easier to do research on many individuals in several locations than to research several individuals in many locations.

4. Systematic Sampling

In systematic sampling sample selection is carried out using intervals. The selection is done systematically or not randomly, except for the first element. In systematic sampling first the entire population must be sorted or numbered first. Then the population is divided into groups, depending on the sample size needed.

For example, in a population there are N individuals and the required sample size is n samples. Create a group where each group has N / n elements, for example k elements. Then randomly select elements from the first group.

Now, after random selection in that group, the selection of elements in the other groups is carried out at intervals. For example the elements in the first group are numbered n1, then the elements in the second group are numbered n1 + k, the elements in the third group are numbered n1 + k + k, and so on.

5. Multi Stage Sampling

Multi stage sampling is a combination of the sampling techniques mentioned above. First, the population will be divided into clusters. These clusters will be grouped in strata based on the equation. In one stratum, one or more clusters will be chosen randomly. This process continues until the cluster cannot be subdivided.

B. Non-Probability Sampling

6. Convenient Sampling

Convenient sampling is the easiest sampling technique to be applied. Sampling was done based on the availability of participants to be involved in the study. Many researchers rely on this technique because of easier implementation, lower costs, and the time required is relatively fast.

An example of using the technique convenient sampling is asking for the availability of students in a class as research subjects. Despite having a lot of comfort offered, this technique will cause bias because volunteers who are willing can have significant differences with those who are not willing. Therefore, this technique does not guarantee to represent the entire population.

7. Quota Sampling

To use quota sampling researchers need to apply previous standards. So he can choose a sample that can represent the population. The proportion of characteristics in the sample must be the same as the population .

Suppose the sample contains 1000 populations of which 600 are male and 400 are female. From this population, suppose the sample needed is 100 samples. Therefore, out of 100, it must consist of 60 men and 40 women.

8. Purposive Sampling

 Stratified Sampling

This sampling technique is also known as selective sampling . In this technique, researchers can provide an assessment of who should participate in the study. Researchers can implicitly choose subjects that are representative of the population.

This kind of sampling technique is usually used by the media when asking for public opinion. The media will choose a subject that can represent the public. The advantages of purposive sampling are time and cost effective, while the disadvantage is when researchers choose the representative subject wrongly.

9. Snowball Sampling

Snowball sampling is a sampling technique that is often used when the study population is very rare or rare. For this reason, sampling is also difficult. The first step in taking this sample is to select one sample from the population.

Then, the individual who becomes the sample will be asked to recommend other samples that fit the description needed. From one sample, it will develop into other samples. Therefore, this technique is called snowball sampling .


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