Quota Sampling

Quota sampling falls under the category of nonprob-ability sampling. Sampling involves the selection of a portion of the population being studied. In probability sampling each element in the population has a known nonzero chance of being selected through the use of a random selection procedure such as simple random sampling. Nonprobability sampling does not involve known nonzero probabilities of selection. Rather, subjective methods are used to decide which elements should be included in the sample. In non-probability sampling the population may not be well defined. Nonprobability sampling is often divided into three categories: purposive sampling, convenience sampling, and 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 specifie subgroups of the population of interest. 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, 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 interviews with 500 females are obtained before interviews with 500 males, then no further interviews would be conducted with females and only males would be randomly selected and interviewed until the target of 500 males is reached. Females in those 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 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 is present in the household, that individual will be interviewed. If a member of the rarest group is not present in the household, then an individual in one of the other rare groups will be selected. Once the quotas for the rare groups are filled, the interviewer will shift to filling the quotas for the more common groups.

The most famous example of the limitations of this type of quota sampling approach is the failure of the [Page 669]pre-election polls to predict the results of the 1948 U.S. presidential election. The field interviewers were given quotas to fill based on characteristics such as age, gender, race, degree of urbanicity, and socioeco-nomic status. The interviewers were then free to fill the quotas without any probability sampling mechanism in place. This subjective selection method resulted in a tendency for Republicans being more likely to be interviewed within the quota groups than Democrats. This resulted in the sample containing too many Republicans and causing the pre-election polls to incorrectly predict Thomas Dewey (the Republican candidate) as the winner.

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