## What is sampling?

Sampling is the process of selecting entities (e.g., people, organizations) from a population of specific interest. By studying this sample, we can generalize our results back to the larger population from which the sample was chosen.

There are two main types of sampling methods: probabilistic and non-probabilistic. In probabilistic or random sampling, the sample population is selected by a “mechanical” procedure like lists of random numbers. Everyone in the sample has an equal chance of being chosen. The probability of being chosen is 1/*n* (where *n* is the number of units in the population).

On the other hand, non-probabilistic methods use purposeful selection and judgement factors to choose people for the sample population. The issue with non-probabilistic sampling is that its results may be biased since not everyone has an equal chance of being surveyed.

*Check out our sampling blog to learn why good sampling is important.*

## What is quota sampling?

Quota sampling is a non-probabilistic sampling method where we divide the survey population into mutually exclusive subgroups. These subgroups are selected with respect to certain known (and thus non-random) features, traits, or interests. People in each subgroup are selected by the researcher or interviewer who is conducting conducting the survey.

For example, consider the situation where an interviewer has to survey people about a cosmetic brand. His population is people in a certain city between 35 and 45 years old. The interviewer might decide they want two survey subgroups — one male, and the other female — each with 100 people. (These subgroups are mutually exclusive since people cannot be male and female at the same time.) After choosing these subgroups, the interviewer has the liberty to rely on his convenience or judgment factors to find people for each subset. For example, the interviewer could stand on the street and interview people who look helpful until he has interviewed 100 men and 100 women. Or he can interview people at his workplace who fit the subgroup criteria.

**How to get quota sampling right**

Unlike random sampling or stratified sampling, quota sampling has no formal rules or proportions. Follow the steps below to get quota sampling right.

**1. Divide the sample population into subgroups**

These should be mutually exclusive. For example, you might divide a certain student population by their professional degree courses, such as engineering, arts, humanities, and medicine.

**2. Figure out the weightages of subgroups**

The weightage is how much of your sample a given subgroup will be. For example, you can assign a weightage of 25% for engineering students, 30% for humanities students, 15% for arts students, and 30% for students specializing in medicine.

**3. Select an appropriate sample size**

The quota size should be representative of the collective subgroup population. For example, you can select a total sample of 500 students from a population of 50,000 students.

*Read our data collection guide to learn how to select a good sample size.*

**4. Survey while adhering to the subgroup population proportions**

Choose survey respondents based on the weightages allotted in Step 2. For our example, survey engineering students until you reach the specified weightage — 25% of the 500-student sample, which is 125 students. Continue with this process until all the quotas are filled and 500 students have been surveyed.

**When to use quota sampling**

### Study a certain subgroup

Researchers can use quota sampling to study a characteristic of a particular subgroup, or observe relationships between different subgroups.

For example, if a researcher wants to analyze the difference between doctors’ and engineers’ behaviors, he can use quota sampling with two subgroups — one with doctors, and the other with engineers.

### Limited time frame or budget

Quota sampling is useful when the time frame to conduct a survey is limited, the research budget is very tight, or survey accuracy is not the priority.

For example, job interviewers with a limited time frame to hire specific types of individuals can use quota sampling. For example, an interviewer who wants to hire people from particular schools can isolate applicants from those schools into particular subgroups. Similarly, an interviewer who wants racial or ethnic diversity in his hires can separate a huge group of applicants into groups based on a person’s ethnicity or race. Many higher education institutes use quota sampling to diversify their batches.

## Criticism of quota sampling

Quota sampling methods are sometimes criticized because the sampled results can be unreliable at times. Quota sampling relies on the researcher’s judgement in choosing the right subgroups and giving them the right weightages. This means that the researcher’s bias can skew the sample and make it non-representative of the entire population, unlike a random sample. However, quota sampling is generally seen as more reliable than other non-probabilistic methods like convenience sampling and snowball sampling.

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