One research question of interest to me is whether the relationship between characteristics of education and later-life cognition in community-dwelling older adults differs between rural and urban schools, with the hypothesis that measures such as student-teacher ratio may be indicators of different exposure characteristics across region classifications. To investigate this research question, I would incorporate nonproportional quota sampling to initiate a cohort study. Nonproportional quota sampling involves segmentation (defining the strata), setting the minimum size of the quotas, and selection of participants. The difference between quota sampling and stratified random sampling falls in the selection of participants, as stratified random sampling gives all potential participants an equal probability of being included and quota sampling may utilize techniques such as convenience sampling, with sampling ending once minimum quotas and other factors (such as sufficient power) are attained. Advantages of quota sampling include the ability for researchers to specify the minimum number of samples in each category, which may be necessary for potentially sparse data when investigating participants in rural areas. This approach may also reduce bias in the estimation of causal effects if the categories are well-defined and account for enough variation in regional characteristics. However, if there is selection bias into the categories used to define the quotas and factors related to educational exposures and later life cognition predict residence in a particular area, univariate quantities may remain biased. Quota-based cohort studies also may still rely on retrospective reporting of circumstances or early life exposures, making them subject to recall bias as well.