The allocations for a set number of respondents within each subgroup (like gender, race, income, and education, or the cross-section of multiple of these subgroups) to ensure the survey is representative of the population of interest for the given research question.
Civis uses a proprietary algorithm to generate “nested quotas,” rather than the marginalized quota “buckets” used historically in survey research. By using a precise combination of many different demographic characteristics to create these quotas (for instance, four people that are in a certain age bucket AND a certain race AND a certain education level), we acquire higher precision and more accurate demographic representation in our sample, compared to traditional quota buckets which create quotas on one characteristic at a time, such as gender or race.
