NON-PROBABILITY SAMPLING

Non-probability sampling from Shefeenakm

Non-Probability Sampling

Sampling is the use of a subset of the population to represent the whole population or to inform about (social) processes that are meaningful beyond the particular cases, individuals or sites studied.  Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated.  Non-probability sampling does not meet this criterion and, as any methodological decision, should adjust to the research question that one envisages answering.  Non-probability sampling techniques are not intended to be used to infer from the sample to the general population in statistical terms.  Instead, for example, grounded theory can be produced through iterative non-probability sampling until theoretical saturation is reached (Strauss and Corbin, 1990).

Thus, one cannot say the same on the basis of a non-probability sample than on the basis of a probability sample. The grounds for drawing generalizations (e.g., propose new theory, propose policy) from studies based on non-probability samples are based on the notion of "theoretical saturation" and "analytical generalization" (Yin, 2014) instead of on statistical generalization.  Researchers working with the notion of purposive sampling assert that while probability methods are suitable for large-scale studies concerned with representativeness, non-probability approaches are more suitable for in-depth qualitative research in which the focus is often to understand complex social phenomena (e.g., Marshall 1996; Small 2009). One of the advantages of non-probability sampling is it's lower cost compared to probability sampling. Moreover, the in-depth analysis of a small-N purposive sample or a case study enables the "discovery" and identification of patterns and causal mechanisms that do not draw time and context-free assumptions.

 Non-probability sampling is often not appropriate in statistical quantitative research, though, as these assertions raise some questions —how can one understand a complex social phenomenon by drawing only the most convenient expressions of that phenomenon into consideration? What assumption about homogeneity in the world must one make to justify such assertions? Alas, the consideration that research can only be based in statistical inference focuses on the problems of bias linked to non-probability sampling and acknowledges only one situation in which a non-probability sample can be appropriate —if one is interested only in the specific cases studied (for example, if one is interested in the Battle of Gettysburg), one does not need to draw a probability sample from similar cases (Lucas 2014a).

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 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.

The issue of sample size in non-probability sampling is rather ambiguous and needs to reflect a wide range of research specific factors in each case.  Nevertheless, there are some considerations about the minimum sample sizes in non-probability sampling as illustrated in the table below:

Table1

Minimum non-probability sample size

Nature of study	Minimum sample size
Semi-structured, in-depth interviews	5-25
Ethnographic	35-36
Grounded theory	20-35
Considering a homogeneous population	4-12
Considering a heterogeneous population	12-30

Non-probability sampling is most useful for exploratory studies like a 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.

Figure1.Probability Sampling Vs Non-Probability Sampling

Types of non-probability sampling and examples

Convenience Sampling. Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. These samples are selected only because they are easy to recruit and the researcher did not consider selecting a sample that represents the entire population.  

Ideally, in research, it is good to test a 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 the researcher. The researcher can send the survey to students and they would act as a sample in this situation.

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 a sample, conducts research over a period of time, analyzes the results and then moves on to another subject or group of the 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.

 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, the researcher will need only a sample, not the entire population. Further, the 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 are selected using quota sampling.

Judgmental or Purposive Sampling. In judgmental sampling, the samples are selected based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those who he feels are the right fit (with respect to attributes and representation of a population) to participate in the 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 methods can be used in pilot studies.

Snowball Sampling. Snowball helps researchers find a 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.

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 an in-depth analysis.

Advantages of non-probability sampling

Non-probability sampling is a more conducive and practical method for researchers deploying the 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.

Getting responses using non-probability sampling is faster and more cost-effective as compared to probability sampling because the sample is known to the researcher, they are motivated to respond quickly as compared to people who are randomly selected.  

Disadvantages of non-probability sampling

 In non-probability sampling, the researcher needs to think through potential reasons for biases.  It is important to have a sample that represents closely the population.

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.

References

·         Non-probability sampling | Lærd Dissertation. (n.d.). Retrieved 26 August 2019, from http://dissertation.laerd.com/non-probability-sampling.php

·         Non-Probability Sampling | Mathstopia. (n.d.). Retrieved 26 August 2019, from https://www.mathstopia.net/sampling/non-probability-sampling

·         Stephanie. (2015, August 6). Non-Probability Sampling: Definition, Types. Retrieved 26 August 2019, from Statistics How To website: https://www.statisticshowto.datasciencecentral.com/non-probability-sampling/

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