Sampling

 Another important step of research is sampling. Sampling can be defined as to show whether the established hypothesis gives the same answer for everyone. By doing sampling we take advantage of virtual background which is basically to decide our decision by collecting data and try to reach main idea. 

There are types of quantitative sampling methods and qualitative sampling methods. I will define them basically. 

• Quantitative Sampling Methods:

- Simple Random Sample 

This is an ideal method for sampling. In this method, first of all we list variables and then we choose one of them randomly. Hence, all variables equally liked to be selected. Thanks to these properties, simple random sampling is unbiased and representative but it can be expensive when we are dealing with people. 

- Convenience Sample:

Convenience sampling is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. So, we choose our interests. 

- Systematic Sampling

In this sample method we choose random point. After this random point, we choose every kth variable which enables us a systematically way to take objects. Thanks to this we have a god approximation of our random sample. Sometimes, we can choose one type more because of our order. 

- Cluster Sampling

In this method we divide population into clusters. After doing this, we chose one of these clusters randomly. So, this method is more convenient and practical than simple random sampling.  If clusters are different there can be bias or non-representativeness. 

- Stratified Sampling

It is similar with cluster sampling but in stratified sampling groups are chosen specifically which represent different characters in population. Ethnicity, location, age, occupation can be examples of stratified sampling. In this method, sometimes for proportion the size of group is important otherwise it is so difficult to administer. 

• Qualitative Sampling Methods:

- Intensity Sampling

Intensity sampling can allow the researcher to select a small number of rich cases that provide in depth information and knowledge of a phenomenon of interest. Then we compare our groups according to good or bad, small or large, etc. 

- Homogenous Sampling

Homogeneous sampling is a purposive sampling technique that aims to achieve a sample whose groups share the same or very similar characteristics or traits. Thus, we compare groups based on their common characteristics.

- Snowball Sampling 

Snowball sampling is a non-probability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus, the sample group is said to grow like a rolling snowball until desired number is reached. 

- Random Sampling

In a random sample the nature of the population is defined and all members have an equal chance of selection. Random sampling and area sampling are variants of random sampling, which allow subgroups to be studied in greater detail. Therefore, this is sufficient if a randomly selected subgroup of the group provides the required properties.