Non-probability sampling: Definition
Non-probability sampling is a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection.
In non-probability sampling, not all members of the population have a chance of participating in the study unlike probability sampling, where each member of the population has a known chance of being selected.
Non-probability sampling is most useful for exploratory studies like pilot survey (a survey that is deployed to a smaller sample compared to pre-determined sample size). Non-probability sampling is used in studies where it is not possible to draw random probability sampling due to time or cost considerations.
Non-probability sampling is a less stringent method, this sampling method depends heavily on the expertise of the researchers. Non-probability sampling is carried out by methods of observation and is widely used in qualitative research.
Learn More: Types of sampling for social research
Types of non-probability sampling and examples
1. Convenience Sampling: Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to researcher. These samples are selected only because they are easy to recruit and researcher did not consider selecting sample that represents the entire population.
Ideally, in research, it is good to test sample that represents the population. But, in some research, the population is too large to test and consider the entire population. This is one of the reasons, why researchers rely on convenience sampling, which is the most common non-probability sampling technique, because of its speed, cost-effectiveness, and ease of availability of the sample.
An example of convenience sampling would be using student volunteers known to researcher. Researcher can send the survey to students and they would act as sample in this situation.
2. Consecutive Sampling: This non-probability sampling technique is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of sample, conducts research over a period of time, analyzes the results and then moves on to another subject or group of subject if needed.
Consecutive sampling gives the researcher a chance to work with many subjects and fine tune his/her research by collecting results that have vital insights.
3. Quota Sampling: Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization. These 500 employees are known as population. In order to understand better about a population, researcher will need only a sample, not the entire population. Further, researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.
For studying the career goals of 500 employees, technically the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since, this is unlikely, the groups or strata is selected using quota sampling.
4. Judgmental or Purposive Sampling: In judgmental sampling, the samples are selected based purely on researcher’s knowledge and credibility. In other words, researchers choose only those who he feels are a right fit (with respect to attributes and representation of a population) to participate in research study.
This is not a scientific method of sampling and the downside to this sampling technique is that the results can be influenced by the preconceived notions of a researcher. Thus, there is a high amount of ambiguity involved in this research technique.
For example, this type of sampling method can be used in pilot studies.
5. Snowball Sampling: Snowball sampling helps researchers find sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, they are asked for assistance to seek similar subjects to form a considerably good size sample.
For example, this type of sampling can be used to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer other subjects suffering from the same ailment to form a subjective sample to carry out the study.
Learn more: How to Determine Sample Size
When to use non-probability sampling?
- This type of sampling is used to indicate if a particular trait or characteristic exists in a population.
- This sampling technique is widely used when researchers aim at conducting qualitative research, pilot studies or exploratory research.
- Non-probability sampling is used when researchers have limited time to conduct researcher or have budget constraints.
- Non-probability sampling is conducted to observe if a particular issue needs in-depth analysis.
Advantages of non-probability sampling
1. Non-probability sampling is a more conducive and practical method for researchers deploying survey in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers. However, if done correctly, non-probability sampling can yield similar if not the same quality of results.
2. Getting responses using non-probability sampling is faster and more cost-effective as compared to probability sampling because sample is known to researcher, they are motivated to respond quickly as compared to people who are randomly selected.
Disadvantages of non-probability sampling
1. In non-probability sampling, researcher needs to think through potential reasons for biases. It is important to have a sample that represents closely the population.
2. While choosing a sample in non-probability sampling, researchers need to be careful about recruits distorting data. At the end of the day, research is carried out to obtain meaningful insights and useful data.
Learn more: How to Conduct Probability Sampling