Quota Sampling Method In Research

Non-probability sampling means researchers subjectively choose the sample instead of using random selection. Not everyone has an equal chance to take part.

Researchers assign quotas to subgroups of the population. Each quota represents that subgroup’s share of the target population as a whole.

Common quota characteristics include gender, age, sex, residency, education level, and income. Once the subgroups are set, researchers use their own judgment to select participants from each one until the quotas are filled.

Correct proportions matter. If the wider population is 65% female and 35% male, the sample should reflect those percentages too.

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 allowed to complete a survey depending on characteristics such as age, gender, or race.
• Researchers rarely have access to an entire population, so they draw the sample from a sampling frame: the accessible list of people they can select from (e.g. a customer database).

How to Use

Here’s a basic outline of how quota sampling might work in a study:

Identify Strata and Proportions

The first step in quota sampling is to identify the strata of the population. Strata are subgroups or categories within the population.

The researchers would then determine the proportions of these strata within the population, which would be the sample’s target proportions.

Select sample size

Several factors, including the population size, the margin of error, the confidence level, and the expected response distribution, determine the sample size in a research study.

Select Participants

The researchers then select participants from each stratum until its quota is filled.

This can be done in several ways. Researchers might randomly draw names from a sampling frame list, approach whoever is accessible, or send out a survey and use the first responses that arrive.

Because the final choice is usually left to the researcher, not chance, the same interviewer bias described under Limitations can creep in here too.

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 selection within each quota is non-random, the interviewer chooses who to approach. This can introduce interviewer bias: researchers tend to pick people who look approachable or convenient, rather than a true cross-section of the subgroup.

This selection bias also means quota sampling cannot support formal statistical inference about the wider population.

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.

Example Situation

Suppose we are conducting a study on the reading habits of high school students in a district. The district’s high school population is 45% freshmen, 25% sophomores, 20% juniors, and 10% seniors.

1. Identify Strata and Proportions: We identify the grade level as our stratum. The proportions are 45% freshmen, 25% sophomores, 20% juniors, and 10% seniors.
2. Select Sample Size: We decide to survey 500 students in total, based on the size of the district’s high school population.
3. Select Participants: Our quotas require 225 freshmen, 125 sophomores, 100 juniors, and 50 seniors, selected by approaching students from each grade until each quota is filled.

Real-Life 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).

A Landmark Example: Gallup and the 1936 US Election

Quota sampling has one especially famous success story. Ahead of the 1936 US presidential election, the magazine Literary Digest mailed about ten million straw-vote ballots. The ballots were drawn from car-registration, telephone and club-membership lists, and the magazine predicted a landslide win for the Republican candidate.

The magazine’s sample was enormous but badly skewed: car and telephone owners were disproportionately wealthy, so the poorer, Democratic-leaning voters of the Depression era were largely excluded. Landon lost in a historic landslide.

In the same election, researcher George Gallup correctly predicted the result using a quota sample of only around thirty thousand people, built to match the voting population’s known proportions.

The episode is still used to teach a key lesson in sampling. A smaller, well-targeted quota sample can outperform a huge but biased one, because representativeness, not sheer size, is what licenses generalisation.

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

Key Terms

• A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. An entire population tends to be too large to work with, so 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.

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.