Non-probability sampling is a method where some elements of the population have no chance of being selected or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Babbie (2007), Stuart (2009) and Leedy (2013) argue that the selection of elements is non-random, non- probability sampling that does not allow the estimation of sampling errors The methods include Quota Sampling, Accidental or Convenience Sampling, Purposive or Judgemental Sampling and Snowball Sampling. In addition, non response effects may turn any probability design into a non probability design if the characteristics of non-response are not well understood, since non response effectively modifies each element's probability of being sampled. Within any of the types of frame identified above Mellenbergh and Adèr et al. (2008) note that a variety of sampling methods can be employed, individually or in combination. Factors commonly influencing the choice between these designs include: nature and quality of the frame; availability of auxiliary information about units on the frame; accuracy requirements, and the need to measure accuracy; whether detailed analysis of the sample is expected; and Cost/operational concerns.
i) Quota Sampling
Kamuzora (2005) notes that quota sampling it is a judgemental sampling with the constraint that a sample includes a minimum number from each specified subgroups in the population. Under this technique Aaker et al. (2012) confirms that the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement 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. It is this second step which Aaker, et al. (2012) thinks is what makes the technique to be one of non-probability sampling. In quota sampling the selection of the sample is non- random. For example, interviewers might be tempted to talk to those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.
ii) Accidental or Convenience Sampling
Babbie (2007), Ghauri and Grønhaug, (2005) view that accidental sampling (also known as grab, convenience or opportunity sampling) is one of the types of non-probability sampling whereby the sample is drawn from the population but very close to hand. That is, a population is selected because it is readily available, willing and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping centre early in the morning on a given day, the people that he/she could interview would be limited to those given by such respondents at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.
iii) Purposive or Judgemental Sampling
A purposive or judgmental sample is selected based on the knowledge of a population and the purpose of the study. According to Babbie (2007) and Dillon, et al. (2005), with purposive sampling a decision with regard to which element, respondent or item to be included in the sample rests on the researcher’s judgement and intuition. The researcher uses a purposive sample because those being interviewed fit a specific purpose. Kumar (2005) find this method as less expensive and quick for selecting a sample although it is prone to biasness. This is because the researcher does not have a real basis for making inferences to a large population as the technique is not based upon a probability model.
iv) Snowball Sampling
Babbie (2007) and Robson (2013) refer to snowball sample according asa sampling technique in which the researcher collects data on the few members of the target population he or she can locate then asks those individuals to provide information needed to locate other members of that population whom they know. For example, if a researcher wishes to interview unregistered immigrants from Somalia to Tanzania, he or she might interview a few unregistered Somalis that he or she knows or can locate and would then rely on respondents to locate more unregistered individuals. A snowball sample is appropriate to use in research when the members of a population are difficult to locate like immigrants. According to Yin (2014), the technique is employed when a researcher is not certain that the respondents have relevant data for the
intended study but he knows a few of them. The researcher interview or provides questionnaires to those few and ask them to identify others who are likely to have the required data.