There are two types of sampling techniques; probability sampling, and non-probability sampling. While you can calculate the probability of a member of the population being selected in probability sampling, it is impossible in non-probability sampling.
For instance, a researcher may be able to calculate that a member has a 10% chance of being selected to participate in the study, while another has 35%.
But in non-probability sampling, each member has an equal chance of being selected even though the chance of participation is not guaranteed.
In this article, we are going to discuss the concept of non-probability sampling, its advantages and disadvantages, and where it can be used.
What is Non-probability Sampling?
Non-probability sampling is defined as a method of sampling in which samples are selected according to the subjective judgment of the researcher rather than through random sampling. Unlike probability sampling, each member of the target population has an equal chance of being selected as a participant in the research because you cannot calculate the probability of selecting anyone.
Non-probability sampling is commonly used in qualitative or exploratory research and it is conducted by observation.
This is because non-probability sampling is a less difficult technique and the outcome depends largely on the expertise of the researcher.
This sampling technique is also used by researchers to save cost or time, especially when it is impossible to use random probability sampling.
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What are the types of Non-probability Sampling?
1. Convenience sampling
This is one of the non-probability sampling techniques where the samples that are readily available in the entire population get selected by the researcher. Convenience sampling is used by researchers because the samples are easy to recruit, and not necessarily because the researcher considers selecting a sample that represents the entire population.
In research, it is important to test the sample that will represent the targeted population. But, in some cases where the population is too large, the researcher may not be able to conduct a test for the entire population. This is why researchers focus on convenience sampling. It is also the most common non-probability sampling method because it is cost-efficient and time-saving.
For example, you ask your students to complete a survey after each of your classes with them. However, the response from your students’ survey does not represent the whole school population.
2. Consecutive sampling
Consecutive sampling is similar to convenience sampling in method, although there are a few differences. In this type of non-probability sampling, the researcher selects a person or a group from the population and conducts research with them over a period of time.
Thereafter, the result from the research is analyzed and then the researcher goes on to another group from the population and conducts another research if necessary. The consecutive sampling technique gives the researcher an opportunity to study diverse topics and gather results with vital insights.
Learn About: Sampling Bias: Definition, Types + [Examples]
3. Quota sampling
To understand quota sampling, let us look at this example. A researcher wants to study the career growth of the employees in an organization with 400 employees. To better understand the population, the researcher will select a sample from the population to represent the total employees or population.
If the researcher is interested in a particular department within the population the researcher will use quota sampling to divide the population into strata or groups. So quota sampling is the division of the larger population into strata according to the need of the research.
For example, if there are 400 women and 100 men, So you will have to select 40 women and 10 men to represent the strata.
4. Judgmental or Purposive sampling
In a judgmental sampling technique, the samples are selected based on the credibility and knowledge of the researcher. This means that only those deemed fit by the researcher are selected to participate in the research.
It is worthy of note that purposive or judgmental sampling is not scientific and it can easily accommodate influence or bias from the researcher. For example, if you want to conduct research about the experience of disabled employees in your large organization, you can select people with special needs in a few departments. Although they serve the purpose, they do not represent your entire employees.
5. Snowball sampling
Snowball sampling is useful for finding samples that are difficult for the researcher to locate. Researchers make use of snowball sampling techniques when their sample size is not readily available and also small.
So this is carried out like a referral program where the researcher finds suitable members and solicits help in finding similar members so as to form a considerably good sample size.
For example, If you want to research the experience of homeless people, considering there is no data to determine their numbers, you can meet one and ask for an audience. If one person agrees, you can ask to be introduced to other homeless people.
Example of Non-probability Sampling
Let us consider some of the examples of non-probability sampling based on three types of non-probability sampling. (quota sampling,
Example 1 (Quota Sampling)
We have earlier established that quota sampling is a method of grouping your sample into strata or groups.
Let us assume that a researcher wants to examine the differences in male and female students of a school with a 20,000 population. To derive a true representative of the larger population from the sample (students), the number of students that the researcher will include in the sample would be based on the proportion of male and female students.
If there are 8000 male students and 12,000 female students. The researcher will select 1200 female students and 800 male students which is proportional to their number. This is the concept of quota sampling.
Example 2 (Convenience Sampling)
A convenience sampling technique is simply one where the people you select for inclusion or as participants in your research sample are those who are most available. Using the example of the 20,000 university students above, let us assume that the researcher is only interested in achieving a sample size of maybe 300 students.
To achieve this, the researcher can stand at one of the main entrances to the lecture rooms or hall, where students passing by can be easily invited to take part in the research. Once the 300 mark is gotten, the researcher may close the door, administer the survey and leave.
It is a very convenient way of gathering sampling participants but is not a good representative of the entire population.
Example 3 ( Purposeful or Judgmental sampling)
Purposeful sampling focuses on the judgment of the researcher and the aim of the research in selecting the sample group. If the aim of the research is to launch beauty products that cater to people with vitiligo, the researcher will then select a few people with this condition as the sample group for the research.
The few people might not entirely be the best representative for the population but they will serve the purpose of the research which is the aim of this technique.
When to use Non-probability Sampling?
- Use this type of sampling to indicate if a particular trait or characteristic exists in a population.
- Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.
- Researchers use it when they have limited time to conduct research or have budget constraints.
- When the researcher needs to observe whether a particular issue needs in-depth analysis, he applies this method.
- Use it when you do not intend to generate results that will generalize the entire population.
Advantages of Non-probability Sampling
The following are the advantages of non-probability sampling:
- It is a more practical and conducive method for researchers that deploy surveys into the real world. Also, non-probability sampling can produce or interpret data in the form of numbers if properly done.
- Responses are faster and cheaper because the sample is familiar to the researcher. So it saves time and resources.
- With non-probability sampling, you can easily connect with your target population especially in an online community.
- Non-probability sampling is also easy to use and you can also use it when you cannot conduct probability sampling perhaps because of a small population.
What are the Disadvantages of Non-probability Sampling?
- A major disadvantage of non-probability sampling is that the researcher may be unable to evaluate if the population is well represented.
- The researcher may be unable to calculate the intervals and the margin of error. This is why most researchers opt for probability sampling first.
- You may also have an unclear sample size because there is no way to measure the boundaries of the relevant population to your research.
What is the Difference between Probability and Non-probability Sampling?
Both probability sampling and non-probability sampling are techniques used to sample members of a population and select them to participate in a study. However, both types of sampling techniques have differences in their processing.
The first thing you should know is that while non-probability sampling gives every member of a population an equal chance of being selected but not everyone has an equal chance of participating in a study, probability sampling does not. This is because probability sampling can be calculated while non-probability sampling cannot. Although everyone has a chance of participating, not everyone has a chance of being selected.
In probability sampling, you can predict the chances a member has of being selected through calculation. Also, probability sampling is based on random selection while non-probability sampling is based on the judgment of the researcher which could be subjective.
Probability sampling is used when the researcher wants to eradicate sampling bias while non-probability sampling does not consider the impact of sampling bias. Non-probability sampling is best considered when your population has similar characteristics while the probability sampling technique is best used when the characteristics of the population are diverse.
Lastly, it is easier to find members to participate in a non-probability sampling because they have similar traits. However, it is not so easy to find suitable participants in a probability sampling because of the need to be diverse.
Conclusion
If you want to conduct research that gives everyone a fair opportunity of participation, then you should consider non-probability sampling. Also, if you are working with a stringent budget, and need to work with a lesser time frame, you should also consider using the non-probability sampling technique.
Furthermore, it is important that you use the right sampling technique for the right research. This is why you should be familiar with the requirements for your study before conducting a survey.