Nonprobability samplingSamplingis the use of a subset of thepopulationto represent the whole population. Probability sampling, orrandom sampling, is a sampling technique in which theprobabilityof getting any particular sample may be calculated.Nonprobability samplingdoes not meet this criterion and should be used with caution. Nonprobability sampling techniquescannotbe used to infer from the sample to the general population.The advantage of non-probability sampling is its lower cost compared to probability sampling. However, one can say much less on the basis of a non-probability sample than on the basis of a probability sample. Of course, research practice appears to belie this claim, because many analysts draw generalizations (e.g., propose new theory, propose policy) from analyses of non-probability sampled data. One must ask, however, whether those published works are publishable because tradition makes them so, or because there really are justifiable grounds for drawing generalizations from studies based on non-probability samples.Some embrace the latter claim, and 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. Non-probability sampling represents a valuable group of sampling techniques that can be used in research that follows qualitative,mixed methods, and evenquantitative research designs. Despite this, for researchers following aquantitative research design,non-probability sampling techniquescan often be viewed as aninferior alternativetoprobability sampling techniques. Non-probability sampling techniques can often be viewed in such a way because units are not selected for inclusion in a sample based on random selection, unlike probability sampling techniques.There are fivetypesof non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level:Quota sampling,Convenience sampling,Purposive sampling,Self-selection samplingandSnowball sampling: Quota sampling: Quota samplingis a method for selecting survey participants. In quota sampling, a population is first segmented intomutually exclusivesub-groups, just as instratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. This means that individuals can put a demand on who they want to sample. This second step makes the technique non-probability sampling. In quota sampling, there is non-random sampleselection and this can beunreliable. Quota sampling is the non probability version of stratified sampling. In stratified sampling, subsets of the population are created so that each subset has a common characteristic, such as gender. Random sampling chooses a number of subjects from each subset with, unlike a quota sample, each potential subject having a known probability of being selected.Convenience sampling: A convenience sample is simply one where the units that are selected for inclusion in the sample are theeasiest to access. That is, a sample population is selected because it is readily available and convenient, as researchers are drawing on relationships or networks to which they have easy access. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.Purposive sampling: The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched. Such samples are biased because prominent experts may differ from other, equally expert, less prominent persons. These purposive sampling techniques includemaximum variation sampling, homogeneous sampling,typical case sampling,extreme (or deviant) case sampling,total population samplingandexpert sampling. Each of these purposive sampling techniques has a specific goal, focusing on certain types of units, all for different reasons.Self-selection sampling: Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in researchon their own accord. The key component is that research subjects (or organisations) volunteerto take part in the research rather than being approached by the researcher directly.Snowball sampling: Snowball sampling is particularly appropriate when the population you are interested in ishiddenand/orhard-to-reach. These include populations such as drug addicts, homeless people, individuals with AIDS/HIV, prostitutes, and so forth. It is anon-probability samplingtechnique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group appears to grow like a rolling snowball. It was widely believed that it was impossible to make unbiased estimates from snowball samples, but a variation of snowball sampling calledrespondent-driven samplinghas been shown to allow researchers to makeasymptoticallyunbiased estimates from snowball samples under certain conditions.