By Julia Simkus, published Jan 07, 2022
Quota sampling is a type of non-probability sampling where researchers will form a sample of individuals who are representative of a larger population.
Non-probability sampling means that researchers choose the sample as opposed to randomly selecting it, so not all members of the population have an equal chance of participating in the study.
Researchers will assign quotas to a group of people in order to create subgroups of individuals that represent characteristics of the target population as a whole.
Some examples are these characteristics are gender, age, sex, residency, education level, or income. Once the subgroups are formed, the researchers will use their own judgment to select the subjects from each segment to produce the final sample.
It is important for researchers to maintain the correct proportions to represent the population. For example, if the larger population is 65% female and 35% male, the final sample should reflect these percentages.
Quota sampling is used when…
- Divide the sample into subgroups depending on any relevant characteristics. For example, you could divide a university population by major.
- Evaluate the proportions of the subgroups to determine the specific elements that will be chosen from each quota. For example, engineering students might be ⅕ of the population.
- Select a sample size. For example, if you are sampling 8,000 students, your quota sample might be 100.
- Choose your participants, adhering to the subgroups characteristics. For example, 20% of your sample should be engineering students.
- Continue with the selection process until your quotas are filled.
Because the sample is representative of the population of interest, quota sampling saves data collection time. It is a quick, straightforward, and convenient way to sample data.
The research costs for this method of sampling are minimal. Researchers save money by using fewer quotas to represent the whole population rather than sampling every individual of a larger population.
The goal of quota sampling is to replicate the population of interest. Researchers will aim to form a sample that effectively represents the population’s characteristics.
Because this method involves non-random sample selection, samples can be biased, making the data less reliable.
While this sampling method can be very representative of the quota-defining characteristics, other important characteristics may not be represented in the final sample group.
Because quota sampling is not a probability sampling method, researchers are unable to calculate the sampling error.
Quota sampling and stratified sampling both involve dividing a population into mutually exclusive subgroups and sampling a predetermined number of individuals from each.
However, the most significant difference between these two techniques is that quota sampling is a non-probability sampling method while stratified sampling is a probability sampling method.
In a stratified sample, individuals within each stratum are selected at random while in a quota sample, researchers choose the sample as opposed to randomly selecting it.
Julia Simkus is an undergraduate student at Princeton University, majoring in Psychology. She plans to pursue a PhD in Clinical Psychology upon graduation from Princeton in 2023. Julia has co-authored two journal articles, one titled “Substance Use Disorders and Behavioral Addictions During the COVID-19 Pandemic and COVID-19-Related Restrictions," which was published in Frontiers in Psychiatry in April 2021 and the other titled “Food Addiction: Latest Insights on the Clinical Implications," to be published in Handbook of Substance Misuse and Addictions: From Biology to Public Health in early 2022.
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