Difference Between Random Sampling And Non Random Sampling With Examples and Meaning
If you are looking to conduct an original study for your research, then you need to choose a method of sampling to get your participants. Choosing an appropriate sampling method is important for qualitative methods and quantitative methods. There are generally two types of sampling methods namely the random sampling method and non- random sampling method. In this article, we will discuss the meaning of random sampling and non-random sampling and the difference between random sampling and non-random sampling with examples.
(Image Will Be Updated Soon)
What Is Simple Random Sampling?
Simple random sampling is the most commonly used probability sampling method. In this sampling method, each unit in the population has an equal probability of being selected. Here, a random sample from a population of any size can be selected. Practically, this becomes very unmanageable. If a unit or subject is drawn from a population and is withdrawn from the subsequent selection, then this process is known as random sampling without replacement. Random sampling with replacement includes returning back the subject or unit to the population so that it has an equal chance of being selected another time.
Let us understand with an example:
Let us understand this method of sampling with an example. The population of Australia alone is approx 2.34 crores. It is not possible to send a survey to every individual to collect information. Here, you can use probability sampling to collect data even if you collect data from a smaller population.
For example, an organization of 40,000 employees residing at different geographical locations. The organization is looking to make some changes in human resource policy. But, before they introduce any change, they want to know whether the employees will be happy with the change or not. However, it is a difficult task to reach out to all 40,000 employees. This is where the probability sample plays a crucial role. A sample from a larger population i.e. 40000 employees will be selected. Now, the sample will represent the entire population. Now, management can survey with this sample. With the responses that are received by employees, management will now be able to decide whether employees in that organization are happy or not about these changes.
What Is Non- Random Sampling?
Non- random sampling is a sampling technique in which samples are selected by the researchers based on their subjective judgment rather than the random selection. This method is highly dependent on the expertise of the researcher and is carried out by observation. Researchers use this method of sampling for qualitative research.
In this sampling, all the members of the population do not retain an equal chance of participating in the study in comparison to probability sampling. This type of sampling is most commonly used for exploratory studies like pilot surveys (surveying a small sample compared to a predetermined size). Researchers use non-random sampling methods when it is not possible to draw random sampling because of the time or cost consideration.
Let us understand the three important methods of non-random sampling that are commonly used with examples.
Convenience Sampling
In this sampling technique, samples are particularly selected on the basis of availability to researchers. This method is used when the availability of samples is rare and expensive. Hence samples are selected based on convenience.
For example, Researchers prefer this method at the initial stage of survey research as it is speedy to deliver.
Purposive Sampling
This method is based on the intention or the purpose of the study. Only those elements will be selected from the population which suits the best for the researcher study.
For example: if you are looking to understand the thought process of the people who are interested in pursuing a master's degree then the selection criteria would be “Are you interested in a Master in Economics?
All the respondents who respond with a “No” will be excluded from the sample.
Quota Sampling
In this method of sampling, researchers create samples involving individuals that represent the population. The individuals in this sampling are chosen according to their specific traits or qualities. They decide and create quotas so that market research samples can be useful in collecting data. These samples can be generalized to the entire population. The final subset of the sample will be decided according to the interviewer's or researcher's knowledge of the population.
For example: if a cigarette company wants to determine what brand of cigarettes are preferred by what age group in a particular city. In such cases, he/she applies quotas on the age groups of 21-30, 31-40, 41-50, and above 51. From this information, the researcher can find the smoking trend among the population of the city.
What Is The Difference between Random Sampling and Non - Random Sampling Techniques?
The difference between random sampling and non-random sampling techniques can be easily understood with basic assumptions of the nature of the population under study. In random sampling, every item has a chance of being selected. Here, the researcher must know the probability that an individual must be selected. Random sampling is the most commonly used for public opinion studies, election polling, and other studies in which results will be applied to a wide-ranging population. This is the situation, whether or not the wide-ranging population is very large, such as the population of an entire country, or small, such as young females living in a specific town.
In non-random sampling, there is a perception that there is an even distribution of populations, This is what makes the researchers believe that any sample can be illustrative and due to this, results will be accurate. As elements in non-random sampling are chosen arbitrarily, there is no chance to estimate the probability of any one element being included in the sample. Also, there is no assurance that each item has a probability of being included. This makes it impossible to estimate even sampling variability or to identify possible bias.
The use of non-probability sampling is most commonly seen during the exploratory stage of a research project, and in qualitative research, which is more biased than quantitative research, but is also used for research with specific target populations in mind, such as farmers that grow rice in the field.
Distinguish Between Random Sampling And Non Random Sampling In Tabular Form
Following are the points representing the distinguish between random and non random sampling in tabular form:
FAQs on Difference Between Random Sampling and Non Random Sampling
1. What is the fundamental difference between random and non-random sampling in statistics?
The core distinction lies in how elements are selected from a population. In random sampling (also called probability sampling), every member of the target population has a known, non-zero chance of being included in the sample. This ensures the sample is representative of the population. In contrast, non-random sampling (or non-probability sampling) involves selecting elements based on non-random criteria, meaning some members might have no chance of selection, leading to potential bias but often used for specific research needs.
2. What is the definition of sampling in the context of statistical studies?
Sampling in statistics refers to the process of selecting a subset of individuals or elements from a larger population. This chosen subset, known as a sample, is then studied to draw conclusions about the entire population. Sampling is crucial when it's impractical or impossible to collect data from every single member of a population, such as in large-scale surveys or quality control in manufacturing.
3. What are the main types of methods used for both random and non-random sampling?
There are several methods under each category:
- Random Sampling Methods: Simple random sampling, stratified random sampling, cluster sampling, and systematic sampling.
- Non-Random Sampling Methods: Convenience sampling, quota sampling, judgemental (or purposive) sampling, and snowball sampling.
4. Why is random sampling often considered more beneficial for researchers compared to non-random methods?
Researchers generally prefer random sampling because it significantly reduces selection bias and allows for the generalization of findings to the entire population. Since every member has a chance of being chosen, the sample is more likely to be representative. This allows for more reliable statistical inferences and conclusions that are broadly applicable, making the research results more robust and trustworthy.
5. How does a researcher typically conduct judgemental sampling, and when is this method most appropriate?
Judgemental sampling, also known as purposive sampling, is carried out by the researcher's own expert judgment and knowledge about the population. The researcher selects participants who they believe will provide the most valuable and relevant information to meet the study's objectives. This method is most appropriate when:
- The research requires specific expertise or insights.
- The population is hard to reach or very specific.
- Qualitative research is being conducted where depth of information is more important than statistical generalizability.
6. Can you provide examples to illustrate the application of random and non-random sampling techniques?
Certainly! Here are some practical examples:
- Random Sampling Example: If a school wants to survey student satisfaction, they could use simple random sampling by assigning a number to each student and using a random number generator to select a specific number of students for the survey.
- Non-Random Sampling Example: A marketing company surveying mall-goers about a new product might use convenience sampling, simply asking people who are easily accessible at a specific time and location. For studying rare diseases, researchers might use snowball sampling, where initial participants refer other eligible participants.
7. What are the key distinctions between systematic sampling and other random sampling techniques?
Systematic sampling is a type of random sampling, but it differs from simple random sampling in its selection process. While simple random sampling involves selecting units entirely by chance, systematic sampling involves selecting every 'n-th' element from a list after a random starting point. For example, if you have a list of 100 students and want a sample of 10, you might randomly pick a starting student (say, the 5th) and then select every 10th student after that (15th, 25th, etc.). This method ensures a well-distributed sample but can be less random if there's a pattern in the list that aligns with the sampling interval.
