Sampling in research is extremely important to define the reliability and accuracy of findings. The probability sampling makes sure that each member of the population has a known probability of being sampled as non-probability sampling techniques do not offer this consideration. Rather, non-probability sampling will be used where the subjects are not determined at random.
These sampling methods are typical in instances where the researchers are not able to ascertain a full list of the population or when the researcher uses qualitative, exploratory, and pilot studies. Within the framework of this blog, we will discuss non-probability sampling, its types, and offer examples of non-probability sampling. Some limitations will also be discussed and how one can make a choice between probability and non-probability methods.
What Is Non-Probability Sampling?
Non-probability sampling can be defined as sampling in a manner that all members in the population do not possess a known or equal probability of selection. The technique is usually applied in cases where there is difficulty or impossibility of establishing a sampling frame (a list of all people thrown). As much as probability sampling is effective in generalizing the results to a larger population, other methods of non-random sampling such as purposive sampling or snowball sampling would be used in exploratory research, case studies or even to study a population which is hard to define.
A disadvantage of non-probability sampling is that it provides biases in the non-probability approach, which results in an incomplete or not reflective representation of the population, which reduces the amount of generalizability of the results. Nevertheless, it remains a significant and very common method, especially in qualitative research where the researcher may be less concerned with the actual generalization (although it is certainly possible), but with exploring or gaining understanding of a specific phenomenon.
Types of Non-Probability Sampling
There are various variants of non-probability sampling which can be applied to research. The strength and weaknesses of each one of them vary with the type of research being carried out. Let us have a closer examination:
1. Purposive Sampling
Purposive sampling can also be referred to as judgmental sampling where individuals are sampled according to certain characteristics or knowledge that is pertinent to the study in question. The researcher hand-picks participants based on their judgment and those who are specifically selected rely on some criteria that are at the core of the research. This is most helpful in case of a particular group or phenomenon that is not common.
Example: Studying the effect of a certain medication, a researcher may only sample the patients with the identified condition that the medication is supposed to be consumed to treat.
Purposive Sampling vs Convenience Sampling:
- In purposive sampling, a certain group is targeted, based on some criterion whereas in convenience sampling, it depends on which participants are easiest to locate.
2. Convenience Sampling
Convenience sampling is used where participants are the ones that one finds easiest to get in contact with or reach. This is a non-random type of sample selection which is applied frequently during pilot studies or in the case of less time, less money and less resources. Although it is rapid and is relatively cheap, it poses a huge bias in the non-probability approaches because the sample is not necessarily predictive of the general population.
Examples: A survey being done in a shopping mall may select the participants who are free and who are willing to answer at the time without reflecting on the wider target group.
Limitations of Non-Probability Sampling:
- The main weak point of convenience sampling is the fact that it frequently contributes to an unrepresentative sample of the population that minimizes the validity of the study.
3. Snowball Sampling
Snowball sampling is a method, which is normally applied in qualitative research to locate the inaccessible groups. Such a researcher starts with a few respondents who fit the requirements of the study and requests them to recommend others who can also be suitable to take part in the research. More people are thus recruited leading to a process known as snowballing.
Example: A researcher who wants to study an individual who has had a rare disease may begin by interviewing a person and requesting him or her to suggest some other individuals with the same disease to interview.
Applications in Exploratory Research:
- Snowball sampling is best suited in exploratory research where there is no need to necessarily build a representative sample, but to have an understanding of a niche or difficult to reach population.
4. Volunteer Sampling
Under the settings of volunteer sampling, people choose to participate in research by themselves. This is mostly applied when the researchers lack control over the individuals involved in the research or when the participants search to be part of the research. Similar to convenience sampling, the lack of representativeness is also a problem with volunteer sampling because the participants are typically motivated or interested in the subject at hand.
Example: A study on the effect of a wellness program might use volunteers, and therefore, create bias by having a sample group of people who have already interest in health and wellness.
Examples of Non-Probability Sampling
We shall look at some real-world non-probability sampling examples.
- Purposive Sampling: A researcher carrying out a study on behaviour of teachers using a given teaching method would decide to interview only those teachers who have tried the method.
- Convenience Sampling: The researcher can perhaps conduct a street survey in which anyone merely asks people who pass by to give their opinion without necessarily putting into consideration their demographic structure.
- Snowball Sampling: A research dealing with individuals who have been homeless may request the initial respondents to introduce others they are aware of who are also in the same situation.
- Volunteer Sampling: A research that is testing a new fitness program may acquire its participants through providing an incentive to recruits who volunteer to participate.
These are just some of the examples of how non-probability sampling may be adopted in acquiring information based upon certain research objectives and limitations.
Choosing Between Probability and Non-Probability Techniques
In the probability vs non-probability sampling debate, the major criteria one should note are the nature of the research being conducted, the resources at their disposal, and the extent to which generalization of the outcome might be needed.
- Probability sampling is better when you require statistical inferences about a population and the reduction of bias and maximizing representativeness is essential. The method is applied whenever a large volume of quantitative research is under investigation and precision is essential.
- Non-probability sampling, however, may often be used in exploratory studies and/or qualitative research, or in cases where a sampling frame is not accessible. The approach may also help when the aim is to focus on a certain group or phenomenon and make no generalizations.
Limitations of Non-Probability Sampling
Although the non-probability sampling is economical and viable when it comes to doing a particular kind of research it has some disadvantages as well:
- Bias: The sample might not reflect the population due to the participants not being picked at random and subsequently there is a possibility of bias.
- Limited Generalizability: Results of non-random samples usually cannot be applied to the overall population, low external validity of the study.
- Subjectivity: In purposive sampling, subjectivity may be brought about by the response of the researcher when deciding on the participants that are to be included in the sampling.
Nevertheless, non-probability sampling has its use in qualitative research, where statistical inference is not the primary concern and understanding of a particular behavior or phenomenon is rather prior.
Applications in Exploratory Research
Non-probability sampling is effective in exploratory studies. The aim in this stage of studies is to collect a very general understanding of an issue or area of interest that leads to a deeper investigation with more sophisticated procedures. To take a case example, a researcher who wants to find out possible problems with a new technology may adopt snowball sampling in order to reveal the experiences of early adopters, which at a later stage may guide more comprehensive research.
Bias in Non-Probability Methods
The main problem with non-probability sampling is that it involves bias that may arise in non-probability methods. Approach to the represented population, in this case, is more likely to have non-representative results, because the volunteers are not selected randomly. Such bias may influence the accuracy and validity of results of the research.
As an example, when using convenience sampling the researcher might only question the residents of a particular area or individuals who belong to a particular demographic. The results may not hold true when applying the results to another area or demographic. On the same note, purposive sampling can lead to skewed samples in the event that the conditions of selecting participants are specific or biased.
Conclusion
Non-probability sampling is also an important procedure or technique in most forms of research, and where the researcher may have some constraints like lack of time, budget, and inaccessibility to conduct research. Although it also brings in bias and is non-generalizable, it is a good method when conducting an exploratory study, qualitative research, and when dealing with difficult-to-reach populations.
Learning about the different kinds of non-probability sampling, including purposive sampling, convenience sampling, snowball sampling, and volunteer sampling, will enable a researcher to select the most effective one according to the case in point. Attention to the shortcomings of the non-probability sampling technique and knowledge of when and how to implement it, can help you to make your research effective and methodologically valid.
