Non-probability sampling is a method of selecting a sample from a population in which not all members have an equal chance of being selected.
Non-probability sampling is often used in qualitative research because it allows the researcher to select participants who will provide the most information about the research topic. This means that the results of the study may not be generalizable to the population as a whole.
Types of Non-probability Sampling
There are four types of Non-probability Sampling:
- Convenience sampling
- Judgmental or purposive sampling
- Snowball sampling
- Quota sampling
- Self-selection or Volunteer Sampling
Convenience sampling is a type of non-probability sampling that relies on the availability of subjects for research. This method is often used in field settings, where researchers have limited time and resources. Convenience sampling can be useful for generating preliminary data, but it is not appropriate for drawing conclusions about a population.
Judgmental or Purposive Sampling
Judgmental or purposive sampling is a type of non-probability sampling that involves selecting units for the sample based on characteristics that are important for the research question at hand.
This type of sampling is often used in exploratory studies or when the researcher has little information about the population of interest. The main advantage of this method is that it can be used to target specific groups that may be hard to reach through other methods. However, a key disadvantage is that the results cannot be generalized to the population as a whole.
Snowball sampling is a non-probability sampling method used when the desired population is hard to reach. The initial subjects are chosen using some non-random method and then asked to refer other potential subjects from the population. The process repeats itself until the desired number of subjects is reached.
This type of sampling is often used in sociology and anthropology, as well as market research. It is useful when researching rarer phenomena or trying to access hidden populations. However, because it relies on referral, snowball sampling can introduce bias and may not be representative of the entire population.
Quota sampling is a type of probability sampling in which the researcher selects a sample based on predetermined quotas for subgroups within the population. This approach is often used when it is difficult to obtain a representative sample using other methods, such as random sampling.
Self-selection or Volunteer Sampling
Self-selection or volunteer sampling is a method of data collection in which participants choose whether or not to participate in a study. This type of sampling is often used in surveys and other research studies. Self-selection can introduce bias into a study, but it can also be used to advantage if the researcher is interested in a particular group of people.
When to use Non-probability Sampling
There are a few key instances in which non-probability sampling should be used in order to get an accurate picture of a population.
- Non-probability sampling should be used when the researcher has a specific target population in mind and wants to study that group specifically.
- It should be used when the researcher wants to study a rare phenomenon or group.
- This type of sampling can be useful when time or resources are limited and a more in-depth study using probability sampling methods is not feasible.
How to Conduct Non-probability Sampling
There are a few different ways to conduct non-probability sampling.
- The first is convenience sampling, which is when the researcher simply uses the subjects that are most convenient or available to him or her. This type of sampling is often used in market research because it can be difficult and expensive to reach potential consumers.
- Another common method is purposive sampling, which involves choosing subjects based on specific characteristics that the researcher is looking for. For example, if a researcher wanted to study how mothers feel about working outside the home, she might use purposive sampling to select mothers who work full-time, part-time, or not at all.
- Snowball sampling is another type of non-probability sampling that can be used when it’s difficult to find subjects who meet the desired criteria.
- Another type of non-probability sampling is quota sampling, which is when researchers select a certain number of subjects from different groups. This could be done by choosing 50 men and 50 women, or 100 people from different age groups.
- Lastly, there’s purposive sampling, which is when researchers choose subjects based on specific characteristics. For example, if a researcher was studying the effects of social media on teenagers, they would purposively sample teenagers who use social media.
Example of Non-probability Sampling
An example of non-probability sampling would be if a researcher were to select a sample of people to study based on their availability, rather than selecting them at random. This type of sampling is often used in situations where it is not possible to obtain a random sample, such as when studying rarer phenomena. Non-probability sampling can also be used deliberately to bias the results of a study, for example by selecting a sample that is known to be unrepresentative of the population.
Importance of Non-probability Sampling
There are a number of reasons for why non-probability sampling is important.
- It does not require a population to be defined as all members of a population have an equal chance of being selected.
- This is in contrast to probability sampling which does require a population to be defined.
- Non-probability sampling is often used when there is no known list of individuals from which to select a sample.
- It can be used when the researcher wants to study a specific group of people, such as those with a certain disease or those who live in a particular geographic area.
- This type of sampling is sometimes used because it is less expensive and easier to collect data from a smaller number of people than it would be to collect data from a larger number of people using probability methods.
Advantages of Non-probability Sampling
Some of the non-probability sampling advantages are:
- Non-probability sampling does not require a population to be identified.
- It can be used when it is not possible or practical to obtain a probability sample.
- Non-probability sampling allows for the inclusion of hard-to-reach populations that may not be represented in a probability sample.
- It can be less expensive and time-consuming to collect than probability samples.
- Non-response bias is eliminated with non-probability sampling since all elements have a known chance of being selected for the sample.
Disadvantages of Non-probability Sampling
There are some disadvantages to using non-probability sampling.
- It can be difficult to determine how to select members of the population, which can lead to bias.
- Non-probability samples are often small, which limits the ability to make generalizations about the population.
- Because non-probability samples are not selected based on random chance, they are often not representative of the population as a whole. This can lead to errors and bias in research results.
- Non-probability samples can be more difficult and expensive to collect than probability samples.
- It can be more difficult to analyze than probability samples because the relationship between the characteristics of the sample and the population is not known.