Quota Sampling Method In Research: Definition and Examples

Non-probability sampling means that researchers subjectively choose the sample instead of random selection, so not all population members have an equal chance of participating.

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.

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 do not have access to an entire population (sampling frame).

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 would then select participants from each stratum until the quota for each stratum was filled.

This could be done in various ways, such as by randomly selecting participants from a sampling frame list, by approaching participants who are accessible, or by sending out a survey and using the first responses that come in.

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.

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. The sample size is based on the total number of individuals in the group you’re interested in studying (population size). For example, if you’re studying students at a university, the population size would be the total number of students there.
3. Select Participants: According to our quotas, we need to survey 225 freshmen, 125 sophomores, 100 juniors, and 50 seniors. We could do this by randomly selecting students from each grade until our quotas are 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).

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.

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.

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.