![]() ![]() The second type is Exponential Non-Discriminative Snowball Sampling where every recruited participant in the research work recruits another participant while in the exponential discriminative snowball sampling not every recruited participant is going to recruit another participant the chain is discriminating. The chain continues to refer linearly up to the end of the sampling. Linear snowball sampling is a snowball sampling in which the researcher recruits a single participant, while the second nominee recruits the third participant. One of the dangers with snowball sampling is that respondents often suggest others who share similar characteristics, or the same outlook, and it is also compulsory on the researcher to ensure that the initial set of respondents is sufficiently varied so that the sample is not skewed excessively in any one particular direction. The researcher is deeply involved in developing and managing the origination and progress of the sample, and seeks to ensure at all times that the chain of referrals remains within limitations that are relevant to the study. 4Īs with random sampling, the snowballing method is not as uncontrolled as its name implied. Common examples of the use of snowball sampling involve sociological studies into hidden populations that may be involved in sensitive issues or illegal activities, such as drug use and prostitution. One of the most well-known forms of non-probability sampling is the snowball sampling method, which is particularly suitable when the population of interest is hard to reach and compiling a list of the population poses difficulties for the researcher. 3 Hence Snowball sampling and respondent-driven sampling allow participants to make estimates about the social network connecting the hidden population. It allows the researcher to make asymptotically unbiased estimates from snowball samples under some conditions. 2 The impossibility of making unbiased estimate from snowball samples was believed, but snowball sampling variation is called respondent-driven sampling. This sampling method generates biased samples because respondents who have great number of social connections are able to provide investigators with a higher proportion of other respondents who have characteristics similar to that initial respondent. ![]() This initial subject serve as “seeds,” through which wave 1 subject is recruited wave 1 subject in turn recruit wave 2 subjects and the sample consequently expands wave by wave like a snowball growing in size as it rolls down a hill. Snowball sampling or Chain-referral-sampling of a hidden population begins with a convenience sample of initial subject, because if a random sample could be drawn, the population would not restrict as hidden. ![]() This paper will review two exemplary non-probability sampling techniques, namely snowball sampling and sequential sampling met Snowball sampling In applied social researches, non probabilistic methods are used especially when random sampling is not theoretically, practically and feasibly sensible. ![]() Unlike probability sampling, non-probability sampling does not involve random selection rather samples are selected based on accessibility. Non-probability sampling techniques help researchers to subjectively select unit that represents the population under study. Thus, a non-probability sampling techniques are needed. In statistical sampling, workforce, time and money highly limits most researchers from getting true random sample that will represent the entire population. It is scientific and every element stands an equal chance of being selected. In statistical sampling (probability sampling technique) calculating the probability of getting any particular sample is possible. Sampling can either be statistical or non-statistical. Representing against population with a subset of it is termed as sampling. ![]()
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