What is Non-Probability Sampling?
Non-probability sampling is a non-random and subjective method of sampling where the selection of the sample’s population elements depends on the personal judgment or the discretion of the sampler.
The distinguishing feature of non-probability sampling is that in such sampling, the selection of population elements is not made through any probability mechanism.
Because of this, the investigator cannot claim that his or her sample is representative of the larger population.
This greatly limits the investigator’s ability to generalize the findings beyond the specific sample studied.
Further, no confidence interval estimation is possible for non-probability sampling.
4 non-probability sampling methods
Convenience Sampling
Non-probability samples that are unrestricted are known as convenience samples.
Researchers or field workers have the freedom to choose whomever they find; thus, the name is convenient. The convenience sample may consist of respondents living in an easily accessible locality.
Undoubtedly, it is the simplest and least reliable form of non-probability sampling. The primary virtue is its low cost.
While a convenience sample cannot ensure precision, this method is frequently used, especially in market research and public opinion surveys.
They are used because probability sampling is often a time-consuming and expensive procedure and in fact, may not be feasible in many situations.
In the early stages of exploratory research, when one is seeking guidance, this approach is recommended.
Accidental Sampling
An accidental type sampling is one in which the selection of the cases is made whatever happens to be available instantly.
In such sampling, individuals are selected as they appear in a process.
Suppose it is decided that only diabetic patients or patients with abdominal pain will be chosen from a queue in front of a hospital counter. In that case, the resulting sample will fall under the accidental sampling procedure.
Purposive Sampling
A non-probability sampling method that conforms to certain criteria is called purposive sampling.
There are two major types of purposive sampling:
- Judgment sampling
- Quota sampling
Judgment sampling
In Judgment sampling, individuals are selected who are considered to be most representative of the population as a whole.
It is a judgment sampling because the choice of the individual units depends entirely on the sampler, who, on his judgment, decides the sample be selected that conforms to some criteria.
In a study of labor problems, you may decide to talk only with those who have experienced discrimination while in a job.
Election results are predicted from only a few selected persons because of their predictive record in past elections.
Quota sampling
A quota sampling is a non-probability sampling in which the interviewers are told to contact and interview a certain number of individuals from certain subgroups or strata of the population to make up the total sample.
The formation of the strata is usually based on such characteristics as sex, age, social status, a region of residence. These characteristics used to form strata are termed ‘quota control’.
The technique is widely used by market researchers, political opinion seekers, and many others to avoid the cost problems of interviewing individuals.
The term ‘quota’ arises from the fact that in this method, the interviewers are given quotas of the sub-groups (i.e. strata) of the population at the very outset to build a sample roughly proportional to the population.
That is, quotas of the desired number of sample cases are computed proportionally to the population sub-groups.
The sample quotas are divided among the interviewers, who then do their best to choose persons who fit the restrictions of their quota controls.
Suppose you want to conduct a survey of rural and urban residents of a population.
How many residents should be chosen from each area?
Suppose it is known that one-third of the population lives in urban areas and two-thirds in rural areas, the sample can be selected purposively from urban and rural areas in the same proportion.
Thus a total of 300 respondents would mean 100 urban and 200 rural residents to be included in the study.
Note that quota sampling may be considered equivalent to stratified sampling with the added requirement that the stratum is generally represented in the sample in the same proportion as in the entire population.
Quota sampling is practiced mainly on the ground that its cost per element is lower than for probability sample, it is easier to administer and can be executed more quickly than a comparable probability sample.
One more apparent advantage of quota sampling is that it can always achieve its intended sample size in each stratum.
In contrast, with a pre-selected random sample, there will always be some selected individuals who cannot be found at home, who have migrated elsewhere, or who refuse to cooperate, resulting in an increased non-response rate.
Despite its simplicity, quota sampling has several weaknesses.
First, the choice of subjects is left to field workers to make on a judgmental basis, and thus it suffers from selection bias.
Secondly, since the procedure for selecting the sample is ill-defined, there is no valid method of estimating the standard error of a sample estimator.
Snowball Sampling
Snowball sampling is the colorful name for building a list or a sample of a special population. Some recent authors have referred to snowball sampling as chain referral or network sampling.
Snowball sampling is conducted in stages. In the first stage, a few persons possessing the requisite characteristic are identified and interviewed.
These persons are used as informants to identify others who qualify for inclusion in the sample. The second stage involves interviewing these persons who can be interviewed in the third stage.
For example, consider the selection of beggars for which no frame is available. This can be best done by asking an initial group of beggars to supply the names of other beggars they come across.
The selection of street sex workers also can be made following this network approach.
If you were able to find a few sex workers willing to talk to you, you might ask them for the names and locations of others they know who might also be willing to be interviewed.
The term snowball stems from the analogy of a snowball, which begins small but becomes bigger and bigger as it falls downhill.
Snowball sampling has been particularly used to study drug cultures, heroin addiction, teenage gang activities, and community relations, and other issues where respondents may not be readily visible or are difficult to identify and contact.
