Since any preconceptions the researcher has are reflected in the sample, large biases can be introduced if these preconceptions are inaccurate. Judgment sampling is subject to the researcher’s biases and is perhaps even more biased than haphazard sampling. In other words, the expert purposely selects what is considered to be a representative sample. An expert with knowledge of the population decides which units in the population should be sampled. With this method, sampling is done based on previous ideas of population composition and behaviour. for qualitative testing, where no attempt is made to generalize to the whole population). Volunteer sampling is often used to select individuals for focus groups or in-depth interviews (i.e. The silent majority does not typically respond, resulting in a large selection bias. Only the people who care strongly enough about the subject one way or another tend to respond. For example, for ethical reasons, volunteers with particular medical conditions may have to be solicited for some medical experiments.Īnother example of volunteer sampling is callers to a radio or television show, when an issue is discussed and listeners are invited to call in to express their opinions. This method can be subject to large selection biases, but is sometimes necessary. Generally, volunteers must be screened so as to get a set of characteristics suitable for the purposes of the survey (e.g. The respondents are only volunteers in this method. Unfortunately, unless the population units are truly similar, selection is subject to the biases of the interviewer and whoever happened to walk by at the time of sampling. ![]() An example of haphazard sampling is the vox pop survey where the interviewer selects any person who happens to walk by. Haphazard sampling assumes that the population units are all alike, then any unit may be chosen for the sample. Units are selected in an arbitrary manner with little or no planning involved. The commonly used non-probability sampling methods include the following. Therefore, data collected using non-probability sampling should be used with extra caution. However, data from non-probability sources have a few challenges with respect to data quality, including the potential presence of participation and selection bias. Some have suggested the possibility of a shift in the paradigm and traditional approach to statistics.
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