Non-probability sampling: Definition, Types, examples, steps, and more

Non-probability sampling

Non-probability sampling is described as a sampling approach in which samples are chosen based on the researcher's subjective view rather than randomly selected. Each person in the population has an equal probability of getting chosen. It's a more lenient approach. The researchers' knowledge is mainly reliant on this sampling strategy. It is carried out by observation, and it is commonly used in qualitative research(Etikan & Bala, 2017).

In contrast to probability sampling, non-probability sampling is a sampling approach in which not all individuals of the population have an equal chance of participating in the research. Every person in the population has an equal probability of getting chosen. For exploratory investigations, such as a pilot survey, non-probability sampling is ideal (deploying a survey to a smaller sample compared to the pre-determined sample size). Researchers utilize this strategy in investigations where random probability sampling is unfeasible owing to time or expense constraints(Tansey, 2009).

There are four main types of non-probability sample

1 Convenience
2 Purposive
3 Quota
4 Snowball

Convenience sampling

Is a non-probability sampling strategy in which samples are chosen from the population only based on their accessibility to the researcher The researchers have chosen these samples only because they are simple to recruit, and they did not consider picking a sample that is representative of the total population(Mweshi & Sakyi, 2020).

In research, it is ideal to test a sample that is representative of the population. However, the population in some studies is too huge to study and consider the complete population. Because of its speed, cost-effectiveness, and ease of availability, convenience sampling, the most common non-probability sampling method, is one of the reasons why researchers rely on it.

Non-probability sampling: Definition, Types, examples, steps, and more Non-probability sampling

Examples of convenience sampling:

A simple example of a convenience sampling approach is when a seller sells promotional booklets and asks questions to selected randomly participants at a shopping center or on a public street.

Businesses employ this sampling strategy to acquire data to address market-related challenges. They also utilize it to gather responses from the sample developed on a single characteristic or a freshly introduced product.

Convenience sampling is commonly used in the early phases of survey research since it is quick and easy to give findings. Even though many statisticians resist using this approach, it is critical in instances where you need insights in a short amount of time or without spending a lot of money(Etikan & Bala, 2017).

Purposive Sampling

Purposive sampling, generally defined as judgmental, selective, or subjective sampling, is a group of non-probability sampling in which researchers choose people from the public to participate in their surveys based on their own opinion.

Purposive sampling is used by researchers when they wish to reach a certain group of people, as all survey participants are chosen because they meet a specific profile. Purposive sampling can be done in many several ways. When generating the sample, all a researcher has to do is eliminate those who do not meet a specific profile.

Purposive Sampling Examples:

Here's an illustration of how market research purposive sampling works:

An organization performs pilot testing to gain market input before introducing a new wine product. The researcher selects skilled wine tasters as part of the sample population to give useful feedback for product enhancement.

Educational research can also benefit from the use of purposeful sampling. Assume you wish to get feedback from kids on their school's educational practices. You go ahead and handpick the smartest pupils who can contribute to your methodical inquiry with pertinent information.

Quota Sampling:

Quota sampling is a non-probability sampling technique in which researchers generate a sample of people who represent a population. These people were chosen by the researchers based on certain characteristics or features. They decide on quotas and set them up so that market research samples may be used to collect data. These samples can be used to estimate the population as a whole. Only the interviewer's or researcher's understanding of the population will determine the final subgroup.

Example of quota sampling

For example, a drug producer would wish to know which age group in a city loves which brand of smokes. Quotas are applied to the age ranges 21-30, 31-40, 41-50, and 51+. The researcher uses this data to estimate the smoking rate in the city's population(Berndt, 2020).

Snowball Sampling

Snowball sampling (also known as chain sampling, chain-referral sampling, or referral sampling) is a nonprobability sampling approach in which current research participants recruit prospective study participants from their circle of friends. As a result, the sample group is said to expand like a snowball. As the sample grows, enough information is collected to be valuable for study. This sampling approach is frequently utilized in difficult-to-reach populations, such as drug users or sex workers. Snowball samples are prone to a variety of biases since sample members are not chosen from a sampling frame. (Goodman, 1961)

Example of snowball sampling

People with a large number of friends, for example, are more likely to be included in the study. This approach is known as virtual snowball sampling when it is employed with virtual social networks. (Goodman, 1961).

References

Goodman, L.A. (1961). "Snowball sampling". Annals of Mathematical Statistics. 32 (1): 148–170. doi:10.1214/arms/1177705148.
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149
. ^ "Snowball Sampling". Experiment-resources.com. (accessed 8 May 2011).
. Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226.
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149.
Goodman, L. A. (1961). Snowball Sampling. The Annals of Mathematical Statistics, 32(1), 148-170, 123. https://doi.org/10.1214/aoms/1177705148
Mweshi, G. K., & Sakyi, K. (2020). Application Of Sampling Methods For The Research Design. Archives of Business Review–Vol, 8(11).
Tansey, O. (2009). Process tracing and elite interviewing: a case for non-probability sampling. In Methoden der vergleichenden Politik-und Sozialwissenschaft (pp. 481-496). Springer.