Sampling methods in Research
Sampling methods are a procedure of selecting units from a wide population. Ph.D. Scholars often face issues in drawing valid conclusions the main reason for which is the wrong selection of samples. They have a question on how to select a sample that is representative of the population. Our research paper writing professionals are providing students with an explanation of different sampling methods.
Sample in Research: Definition
When you are asked to perform research on a specific topic, it is almost impossible for you to gather data from every person. Therefore, if you want to collect data from a large number of people then in such case you need to select a sample of people which represents the population. Sample in Research is a group of people who are going to participate in the study. Sample in Research can be referred to as a specific group of people from whom you want to collect information about a particular topic.
A careful selection of participants is very much essential for drawing a valid conclusion. It is very much important for you to ensure that participants which you are selecting for research represent the entire population.
Meaning of Population in Research
The population in research refers to a group of people about whom you intend to conclude. You can define the population about geographical location, income, age, and other characteristics.
For example, the Researcher may execute a study for making interferences about complete adult population in the nation. Your study might emphasize on customers of particular brands, people with special kinds of disabilities, etc. It is very much essential for you to properly define the target population on the basis of the purpose and practicalities of the research project. In case the population is very large and belong to different demographics or geographical location then it may be very much difficult for you to gain access to representative sample.
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What is the sampling Frame?
The sampling frame can be referred to as an actual list of people or populations from where you will select the participants. It includes a complete population.
Example: In case you are executing a study for gathering information about the working condition of Arc organization, your target population could be 500 workers working in a company. The sampling frame you can select is the Human resource manager in a firm. As they are people those who have name and lists of all employees.
Definition of sample Size
The sample size is several people in the sample which is based on the size of the population. It is also dependent on the size of the population and on the way you intend the results to represent the population.
The researcher generally uses the Sample Size Calculator for determining the extent up to which the sample should be. In the context of a large sample size, you can easily make inferences about the complete population inaccurate and confident manner.
Different types of sampling methods: Examples
Sampling methods are a technique that the researcher utilizes for selecting a few people as participants in research. There are 2 types of sampling methods are Probability and non –probability.
1. Probability sampling
It means that each individual has a chance to get selected as a participant in research. The probability sampling method is considered appropriate in the case when you intend to generate research results that are representative of the complete population. Probability sampling technique can further categorize into 4 these are:’
A) Simple Random sampling
In the context of simple random sampling, each person in a population has an equal chance to get selected in research. You need to include a complete population in your sampling frame. You can utilize different tools such as random number generator or other techniques which is based completely on chance.
Example: You intend to select a simple random sample of 50 employees of company ABC. You are requiring assigning a number to each worker in an organization database from 1 to 500. You can utilize a random number generator for selecting 50 numbers.
B) Systematic Sampling
It is the same as a simple random sampling technique. As compared to other sampling techniques, it is easier to perform systematic sampling. You need to list each member of a population by giving them a number. You need to select a person at regular intervals.
Example: You can list all workers in alphabetical order. You select people as participant’s random sampling from the starting 10 numbers. The researcher can select 5 numbers using a random sampling technique. From 5th number onwards, you can select every 10th individual as a participant for study.
If you intend to utilize a systematic sampling technique, then you must ensure the elimination of hidden patterns. For example, Human resource managers in an organization list workers based on groups. Groups’ members are listed according to their seniority.
C) Stratified sampling
You can utilize such type of sampling technique in the case when the population has mixed characteristics. In such a case it is very much essential for you to make sure that every character is proportionate and represents a sample.
You can categorize a large population in small subgroups based on relevant characteristics.
From the complete proportions of the population, you are required to compute the way many should be sampled from every subgroup. Then you can utilize random or systematic sampling for selecting a sample from every subgroup.
Example: An organisation has 400 female workers and 600 boys’ employees. You intend to make sure that the sample which you have selected reflects the gender balance of an organization. So you can sort the population into 2 strata on the basis of gender. Then you can utilize random sampling on every group you can select 40 female and 60 boys, which will provide you with a representative sample of 1000 people.
D) Cluster Sampling
It includes categorizing the population in subgroups. But it is very much important for you to make sure that every sub-group has the same characteristics. Instead of sampling every person in different subgroups, you can select the entire subgroup using a random sampling technique.
If it is possible then you can include each individual from every sampled cluster. In case the cluster is large then you can sample every person from within the cluster.
Cluster sampling is effective in case when you want to deal with large and dispersed populations. But there is a high chance of risk in applying cluster sampling technique, as there could be a great difference between clusters. It is very much difficult to guarantee that the sample which you have selected represents the entire population.
Example: An organisation has offices in 20 cities across the nations .But you could not have the potential to travel every office of enterprise for collecting information, so you can utilize a random sampling technique for collecting information. You can select 3 offices for selecting sample as these are your clusters.
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Non- probability sampling:
In non-probability sampling participants are selected using non-random criteria. It means that all the person in the population does not get a chance to get selected as participants in research. Non –probability sampling is considered as the easiest and cheapest form of sampling technique. The biggest drawback of non- random sampling is that you can utilize it for making valid statistical interferences related to the complete population.
Non-probability sampling technique is most suitable for selecting a sample in exploratory and Qualitative research. In such type of research, the main aim of the Test is not to test a hypothesis about a broad population. The 4 different types of non-probability sampling technique are:
A) Convenience Sampling
A convenience sample involves all people who happen to be most accessible to the investigator
Convenience sampling is considered as the easiest and inexpensive technique for selecting sample participants. In this sampling method also, you cannot ensure that sample that you have select represents the population. Therefore, you can’t produce generalize results.
Example: Researcher is performing study for gathering information about students’ perception about Student support services offered by University. An investigator after completion of each class asks students to fill a survey form containing questions related to specific topics. It is one of the most convenient ways of gathering information. As the researcher is performing a survey by selecting students attending the same classes at the same level then you cannot ensure that the sample represents all the students of college or University.
B) Voluntary response Sampling
This sampling method is somewhat the same as convenience sampling. Besides selecting participants and directly contacting them you can volunteer yourself. If you are utilizing the voluntary response sampling technique then there is a high chance of biasness in response.
Example: Researcher sends survey form to each and every student of university and college. All the students show their interest in filling the survey form. By getting the response from a large number of students you will able to develop an in-depth understanding of the topic. You can make assumptions that people those who are filling survey form have strong opinion related to students support services. Therefore, you can‘t make sure that the opinion of a few students represents a view of all students.
C) Purposive sampling
It is a type of sampling which includes an investigator utilizing their judgment for selecting a sample that is most useful for achieving the objective of the research. The researcher generally utilizes a purposive sampling technique for selecting a sample in Qualitative research. If in case you intend to make effective use of purposive sampling you need to set proper criteria.
Example: If you want to gather information about views and experiences of students suffering from special kind of disability, then you by utilizing purposive sampling technique can make a selection of participants.
D) Snowball Sampling
In case, if you find it difficult to access participants, you can utilize a snowball sampling technique for choosing people for gathering information. In the snowball sampling method you can seek support from other people to have access to other members in the target group. Example: A researcher is performing an investigation for gathering information about homelessness in a city. Since you don’t have information about such people in a city, therefore, you can not apply the probability sampling technique. The researcher suddenly meets a person who showed interest in participating in research. He or she helps the researcher in contacting with other homeless people.
It has been concluded from above that selection of the right sample is very much important for producing valid conclusions. Another fact which has been concluded from the above is that non-probability sampling is more suitable for performing exploratory and Qualitative research.