NON-PROBABILITY SAMPLING
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/
This is truly a valuable resource that greatly aids in advancing research capabilities, especially for budding researchers in the ranches. Thank you very much for this.
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