What is Quota Sampling?
In probability sampling, scientists need to understand the effects of a phenomena on a specific population. Sometimes, however scientists try and structure their sampling techniques to ensure subgroups of a population have equal chances of being selected for the sample population group. This is a process known as non-probability sampling, and the technique is known as quota sampling.
How does Quota Sampling work?
Quota sampling works by first dividing the selected population into exclusive subgroups. The proportions of each subgroup are measured, and the ratio of selected subgroups are then used in the final sampling process. The proportions of the selected subgroups are used as boundaries for selecting a sample population of proportionally represented subgroups. Scientists will often go back over the selected sample population to ensure that the subgroups are represented to the same extent that they are in the larger population.
There are some disadvantages to quota sampling as the definitions of a subgroup are typically limited to a couple of traits. Accordingly, other traits associated with the population may become over-expressed. For example a population divided by socioeconomic status will have a different representation as a population divided by eye color. It is important for scientists to divide the population into subgroups as closely relevant to the phenomena being studied.
Quota Sampling and Machine Learning
algorithms can be used in both the identification and analysis of quota sampling. Imagine scientists wanting to look at the effects of socioeconomic status on academic performance. Using a machine learning algorithm, the scientists can divide up the population of students into subgroups of socioeconomic status, record the ratios, and select a
proportionally representative sample group from the larger population in a relatively short period of time. Because machine learning algorithms are good at processing large amounts of data, it can be used in conjunction with quota sampling as an effective way of understanding the effects of phenomena on a population.