Quota Sampling: Definition, Method and Examples

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 instead of randomly selecting it, so not all population members have an equal chance of participating in the study.

Key Terms

  • A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
  • Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics (e.g., gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize various sampling methods.
  • Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

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.

Types

Controlled Quota Sampling

  • In controlled quota sampling, there are limitations on the researcher’s choice of samples.

Uncontrolled Quota Sampling

  • In uncontrolled quota sampling, there are no restrictions on the researcher’s choice of samples. Researchers are free to choose sample members at their own will.

Applications

Quota sampling is used when…

  • Time is limited as quota sampling is a quick method of sampling.
  • The budget is tight as it is cheaper than other sampling methods.
  • Researchers have specific criteria or constraints for conducting their research.
  • Researchers want to monitor the number of participants that are allowed to complete a survey depending on characteristics such as age, gender, or race.
  • Researchers do not have access to an entire population.

How to Use

  1. Divide the sample into subgroups depending on any relevant characteristics. For example, you could divide a university population by major.
  1. 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.
  2. Select a sample size. For example, if you are sampling 8,000 students, your quota sample might be 100.
  3. Choose your participants, adhering to the subgroups’ characteristics. For example, 20% of your sample should be engineering students.
  4. Continue with the selection process until your quotas are filled.

Advantages

Quick and easy

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.

Cheap

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.

Representative of target 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.

Limitations

Large potential for bias

Because this method involves non-random sample selection, samples can be biased, making the data less reliable.

Not generalizable to the population

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.

Cannot calculate sampling error

Because quota sampling is not a probability sampling method, researchers are unable to calculate the sampling error.

Examples

  • Ensuring that an adequate number of midlife women were recruited from the targeted ethnic groups in an Internet based study (Im & Chee, 2011).
  • Recruiting at-risk Women for microbicide research and ensuring adequate representation of specific sample characteristics (Morrow et al., 2007).
  • Obtaining a representative sample of pregnant women to study trends in smoking during pregnancy in England (Owen, McNeill, & Callum, 1998).
  • Recruiting respondents to participate in an interview about stress levels with quotas based on sex, age, working status, residential location, housing tenure, and ethnicity (Sedgwick, 2012).
  • Monitoring national trends of tobacco smoking in France (Guignard et al., 2013).
  • Quantifying the use of sunbeds in children across England and identifying geographical variation to study the rise of malignant melanoma (Thomson et al., 2010).

Quota Sampling vs Stratified Sampling

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 witin each stratum are selected at random while in a quota sample, researchers choose the sample as opposed to randomly selecting it.

References

Boston University School of Public Health. (n.d.). The role of probability. Sampling. Retrieved from https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_probability/bs704_probability2.html

Guignard R, Wilquin J-L, Richard J-B, Beck F (2013) Tobacco Smoking Surveillance: Is Quota Sampling an Efficient Tool for Monitoring National Trends? A Comparison with a Random Cross-Sectional Survey. PLoS ONE 8(10): e78372. https://doi.org/10.1371/journal.pone.0078372

Im, E. O., & Chee, W. (2011). Quota sampling in internet research: practical issues. CIN: Computers, Informatics, Nursing, 29(7), 381-385.

Morrow, K.M., Vargas, S., Rosen, R.K. et al. (2007). The Utility of Non-proportional Quota Sampling for Recruiting At-risk Women for Microbicide Research. AIDS Behav 11, 586. https://doi.org/10.1007/s10461-007-9213-z

Owen, L., McNeill, A., & Callum, C. (1998). Trends in smoking during pregnancy in England, 1992-7: quota sampling surveys. Bmj, 317(7160), 728-730.

Quota sampling: Definition, types & free examples. QuestionPro. (2021, July 19). Retrieved from https://www.questionpro.com/blog/quota-sampling/

Quota Sampling. Voxco. (2021, March 12). Retrieved from https://www.voxco.com/blog/quota-sampling/

Sedgwick, P. (2012). Proportional quota sampling. BMJ, 345. https://doi.org/10.1136/bmj.e6336

Thomson, C. S., Woolnough, S., Wickenden, M., Hiom, S., & Twelves, C. J. (2010). Sunbed use in children aged 11-17 in England: face to face quota sampling surveys in the National Prevalence Study and Six Cities Study. Bmj, 340.

Saul Mcleod, PhD

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Educator, Researcher

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education.

Julia Simkus

Research Assistant at Princeton University

Undergraduate at Princeton University

Julia Simkus is a Psychology student at Princeton University. She will graduate in May of 2023 and go on to pursue her doctorate in Clinical Psychology.