Sampling is the process of selecting a subset from a population, and it’s an important component in research. It can impact the validity, reliability, and generalizability of your data. This article will discuss more about sampling and some of these methods to help you decide which one might be best for your study.
Sampling is basically a statistical analysis technique that you can apply for selecting the predetermined number of a sample from a large population having similar characteristics. When you perform the investigation, due to lack of resources and time you would not able to gather information from each and everyone, in such cases you can use the sampling method for selecting participants.
Besides gathering facts from all people individually you can select the sample. The researcher uses the term sample is for a group of people who have select as participants in research.
In simple words, Sampling is the procedure that you can use for selecting a few people from a large population as participants in research.
The goal of sampling is to select parts or groups within the population and study those parts in order to determine information about all of the group as a whole, such as reliability, validity, and accuracy.
The sampling method can be categorized into two, these are:
There are multiple uses of both probability and non-probability sampling methods, as follows:
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The different types of probability sampling methods are:
It is a type of probability sampling method by using which you can provide all people in the population with a chance to get select as a participant in the research process. While applying this method you need to make sure that the sampling structure which has been chosen covers the entire population.
It’s reliable because every member in the entire group has the same chance to be chosen, and it saves time by not having to ask everyone for their opinion on something just one at a time.
For example, for conducting the investigation, you are planning to select 100 employees out of a thousand workers in a company. You can give random numbers to all thousand employees and by using the random sampling technique can select 100 workers.
It is a probability sampling method that includes a listing of all people. In a systematic sampling technique, all people are chosen at a regular interval of time.
This type of sampling technique is less time-consuming because it has a predefined range and requires starting point selection, which can be repeated at regular intervals for an accurate representation.
For example, If you want to choose a sample of 100 people out of 1000 employees you can arrange or list them in an alphabetical way. Then from the list of 1000 employees, you can choose every sixth or twelfth person as a participant in the research. Then afterward you can select every fifth person and in this way, you can make the selection of all 100 people.
While making the application of systematic sampling you need to confirm that there is no hidden pattern. For example, if you arrange the database in alphabetical order you are aware of that while performing the sampling.
You can apply the stratified sampling method where the population involves mixed characteristics and you want to make sure that you have represented each character is equally. In this sampling method, the researcher characterizes the entire population on the basis of different characteristics such as on the basis of demographics, job role, etc.
This process divides the population into smaller groups and then draws samples from each group separately, without having to worry about overlapping members or missing some individuals altogether.
For example, An organization consisting of 500 female and 100 male workers. While selecting a sample you want to make sure that the sample demonstrates gender balance then you can categorize the entire population on the basis of gender. Then after that, you can apply a random sampling technique to every group. After that, you need to select 80 women and 20 men.
It is a type of sampling technique that mainly includes categorizing the population into subgroups. While applying this technique you need to make sure that every group consists of similar characteristics. The cluster sampling method is suitable when you have to select a sample from a large population.
The biggest disadvantage of this method is that there are high chances of error in the selection of samples. Another major drawback of this method is that you can’t make sure those samples that you have selected using a cluster sampling technique represent the whole population.
For example, you want to conduct an investigation in order to analyze whether employees working in an organization are satisfied with the working environment. Companies, where you want to perform surveys research, consist of 1000 employees.
Kingfisher airlines has 12 branches located in different cities. In such a case, you can not reach each and every employee for collecting information; therefore you can select 5 offices randomly, which is basically is your cluster.
In non-probability sampling, researchers need to use a non-randomized criterion for selecting the sample. The different types of non-probability sampling methods are:
In this type of sampling technique, the researcher selects those participants who are easily accessible by an investigator. One of the biggest advantages is that it is less expensive and you can collect information easily. The major drawback of the method is that you can’t make sure that the people whom you have select as participants represent the entire population.
For example, using friends or family as part of a sample is easier than targeting unknown individuals. This is a convenient way to gather data.
The voluntary sampling technique is somewhat like a convenient sampling method, instead of relying on chance or random selection, people actively volunteer themselves for participation in studies and surveys. One biggest drawback of this method is that there are high chances of bias ness.
For example, Suppose you by performing the investigation and collecting information about student support services. You could share the survey questionnaire with all the students at the university and a lot of students decided to complete it. But while applying this technique you will not able to confirm that the response provides by few students represents the perspective of other students.
You, in order to apply the purposive sampling method, will require utilizing your personal judgment for selecting the sample. Such type of sampling technique researchers mainly utilizes for performing qualitative research. You can also utilize it when you intend to develop an in-depth understanding of a particular situation. You need to set proper criteria and reasons for inclusion in order to make purposive sampling effective.
For example, you want to perform an investigation for analyzing the academic performance of people suffering from a special type of disability. You can purposefully select disabled people for collecting a huge amount of information about their academic performance.
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If the population is hard to access, you can use snowball sampling to get more people. You can utilize this sampling technique for selecting few people as participants with the support of other people. You start with the people you have and then other people will make contact with other people who are in your network.
For example, You are trying to gather information about homeliness people but you don’t have access to the list. Suddenly you met a person who gets ready to act as a participant in the investigation. You can ask him to provide contact detail of other people.
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