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Sampling involves the selection of a portion of the finite population being studied. Nonprobability sampling does not attempt to select a random sample from the population of interest. Rather, subjective methods are used to decide which elements are included in the sample. In contrast, in probability sampling, each element in the population has a known nonzero chance of being selected through the use of a random selection procedure. The use of a random selection procedure such as simple random sampling makes it possible to use design-based estimation of population means, proportions, totals, and ratios. Standard errors can also be calculated from a probability sample.

Why would one consider using nonprobability sampling? In some situations, the population may not be well defined. In other situations, there may not be great interest in drawing inferences from the sample to the population. Probably the most common reason for using nonprobability sampling is that it is less expensive than probability sampling and can often be implemented more quickly.

Nonprobability sampling is often divided into three primary categories: (1) quota sampling, (2) purposive sampling, and (3) convenience sampling. Weighting and drawing inferences from nonprobability samples require somewhat different procedures than for probability sampling; advances in technology have influenced some newer approaches to nonprobability sampling.

Quota Sampling

Quota sampling has some similarities to stratified sampling. The basic idea of quota sampling is to set a target number of completed interviews with specific subgroups of the population of interest. Ideally, the target size of the subgroups is based on known information about the target population (such as census data). The sampling procedure then proceeds using a nonrandom selection mechanism until the desired number of completed interviews is obtained for each subgroup. A common example is to set 50% of the interviews with males and 50% with females in a random-digit dialing telephone interview survey. A sample of telephone numbers is released to the interviewers for calling. At the start of the survey field period, one adult is randomly selected from a sample household. It is generally more difficult to obtain interviews with males. So, for example, if the total desired number of interviews is 1,000 (500 males and 500 females), and the researcher is often able to obtain 500 female interviews before obtaining 500 males interviews, then no further interviews would be conducted with females and only males would be selected and interviewed from then on, until the target of 500 males is reached. Females in those latter sample households would have a zero probability of selection. Also, because the 500 female interviews were most likely obtained at earlier call attempts, before the sample telephone numbers were thoroughly worked by the interviewers, females living in harder-to-reach households are less likely to be included in the sample of 500 females.

Quotas are often based on more than one characteristic. For example, a quota sample might have interviewer-assigned quotas for age by gender and by employment status categories. For a given sample household, the interviewer might ask for the rarest group first, and if a member of that group were present in the household, that individual would be interviewed. If a member of the rarest group were not present in the household, then an individual in one of the other rare groups would be selected. Once the quotas for the rare groups are filled, the interviewer would start to fill the quotas for the more common groups.

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