How is Quota Sampling Different from Stratified Sampling?
The concept of Quota Sampling plays a key role in mathematics and statistics and is widely applicable to real-life surveys, research studies, and exam scenarios. Understanding quota sampling helps in collecting representative data even when probability sampling is not possible.
What Is Quota Sampling?
A Quota Sampling method is a type of non-probability sampling where the researcher divides the population into exclusive subgroups and sets a specific number or quota for each subgroup to be included in the sample. You’ll find this concept applied in areas such as sampling methods, market research, and survey data collection.
Key Features and Definition
Quota sampling is defined as gathering a sample from a population by first partitioning it into key subgroups (such as age, gender, or location) and then filling a predefined quota of respondents from each subgroup. Researcher judgment is used to select individuals, but the numbers for each group are fixed in advance.
How Quota Sampling Works: Step-by-Step Illustration
- Divide the population into subgroups
(e.g., males and females or age groups). - Determine quota for each subgroup
(e.g., 40% males and 60% females, based on their ratio in the population). - Recruit participants to fill each quota
(e.g., keep collecting data from each subgroup until its set quota is reached). - Combine the results from all groups to form your sample.
Quota Sampling Example
Suppose you are conducting a survey in a college with 60% girls and 40% boys. You want a sample of 50 students using quota sampling.
1. Quota for girls: 60% of 50 = 30 students
2. Quota for boys: 40% of 50 = 20 students
3. Collect responses until you have 30 girls and 20 boys.
Quota Sampling vs. Other Methods
| Method | How Sample is Selected | Statistical Bias? |
|---|---|---|
| Quota Sampling | Divide population into groups, fill a set quota from each (non-random). | Yes, possible |
| Stratified Sampling | Divide population into groups, then select randomly from each. | Usually no |
| Convenience Sampling | Choose whoever is easiest to access | Yes, high |
| Purposive Sampling | Select for a particular purpose, not always by group | Yes, sometimes |
Advantages and Limitations
| Advantages | Limitations |
|---|---|
|
|
Solved Problem Using Quota Sampling
Problem: A school has 500 students (300 boys, 200 girls). You want a sample of 40, with quotas based on current proportions. How many boys and girls should you include?
1. Total students = 500
2. Boys: 300/500 × 40 = 24
3. Girls: 200/500 × 40 = 16
4. So, sample should have 24 boys and 16 girls.
Frequent Errors and Misunderstandings
- Confusing quota sampling with stratified sampling (stratified is random, quota is not)
- Not matching quotas to the real population percentages
- Assuming quota sampling gives unbiased results (it doesn’t guarantee this)
Classroom Tip
A quick way to remember quota sampling: “Set the group, fill the quota, but don’t randomize!” Vedantu’s teachers often use stories or group roleplay to make this idea memorable in live classes.
Try These Yourself
- Suppose a company has 70% engineers, 30% designers. For a survey of 20, how many from each category if using quota sampling?
- Set up a sample quota if a population has 40% children and you want a total sample of 10.
- Why doesn’t quota sampling give a truly random sample? Explain.
Relation to Other Concepts
The idea of quota sampling connects closely with other data collection topics like Types of Data in Statistics and methods for Data Collection and Organization. Mastering quota sampling helps in understanding survey design and sampling errors.
Speed Trick: Quickly Calculating Quotas
To work out the quota for each group, just multiply the group's fraction of the population by your desired sample size. For example:
- If 45% of the population are girls and the sample is 80,
Girls quota = 45% × 80 = 36
We explored Quota Sampling—from its definition, process, and benefits to key mistakes and its relation to other sampling ideas. Practice this concept with other sampling methods on Vedantu to prepare confidently for board and competitive exams.
Smart Internal Links for Further Learning
- Sampling Methods – full overview and comparison with quota sampling
- Types of Data in Statistics – connects quota sampling to data types
- Data Collection and Organization – see how sampling fits into organizing results
- Probability – understand why random selection matters
FAQs on Quota Sampling: Meaning, Method, and Examples
1. What is quota sampling in simple terms?
Quota sampling is a non-probability sampling method where researchers create a sample that mirrors the characteristics of a larger population. First, the population is divided into mutually exclusive subgroups (called quotas) based on traits like age, gender, or location. Then, researchers select individuals non-randomly from each subgroup until the predetermined quotas are filled.
2. What are the main steps involved in conducting quota sampling?
Conducting quota sampling typically involves the following steps:
- Divide the Population: Segment the population into relevant subgroups based on specific characteristics (e.g., age group, income level).
- Determine Proportions: Figure out the proportions of these subgroups in the actual population. For instance, if the population is 60% female and 40% male, the quotas should reflect this.
- Recruit Participants: Select individuals non-randomly (e.g., based on convenience or judgement) until the quota for each subgroup is met.
- Collect Data: Once the quotas are filled, data collection from the sample can begin.
3. What is the most important difference between quota sampling and stratified sampling?
The primary difference lies in the selection method. In stratified sampling, after dividing the population into groups (strata), participants are selected randomly from each group. This ensures every individual has a known, non-zero chance of being selected. In contrast, quota sampling uses non-random selection. The researcher chooses individuals based on convenience or judgement until the quotas are met, meaning the probability of selection is unknown.
4. Could you provide a real-world example of quota sampling?
Imagine a market researcher wants to understand smartphone preferences among 200 university students. They know the university has 60% science students and 40% arts students. Using quota sampling, they would set a quota to interview 120 science students (60% of 200) and 80 arts students (40% of 200). The researcher would then go to campus and survey the first 120 science and 80 arts students they meet, without using a random selection process.
5. What are the key advantages and disadvantages of using quota sampling?
Advantages: It is relatively quick, inexpensive, and easier to manage than probability methods. It is also useful when a complete list of the population is unavailable, and it ensures that specific subgroups are not left out of the sample.
Disadvantages: The main drawback is the high risk of selection bias, as the sample is not chosen randomly. This means the findings cannot be reliably generalised to the entire population, and it's impossible to calculate the margin of error.
6. How exactly does non-random selection in quota sampling introduce bias?
Bias enters quota sampling because researchers make choices based on convenience and accessibility. For example, a researcher might approach people who look friendly, are in easily reachable locations (like a mall entrance), or are available during specific times. This systematically excludes individuals who are less accessible or do not fit the researcher's subconscious criteria, leading to a sample that does not accurately represent the entire population's diversity of views and behaviours.
7. Why are findings from quota sampling not considered statistically generalisable?
Findings are not considered statistically generalisable because the sample is not a true random representation of the population. Without random selection, it is impossible to calculate the sampling error—the statistical measure of how much the sample results might differ from the actual population's results. Since we cannot quantify this uncertainty, we cannot confidently claim that what is true for the sample is also true for the entire population.
8. In what situations would a researcher choose quota sampling over a more robust method like stratified sampling?
A researcher might prefer quota sampling in situations where:
- Speed and Cost are Critical: It is much faster and cheaper to implement than methods requiring a complete population list and random selection.
- Exploratory Research: It is useful for gaining initial insights or testing a hypothesis before committing to a larger, more expensive study.
- No Sampling Frame Exists: When it is impossible to get a complete list of the entire population (a sampling frame), probability sampling is not feasible.
- Practical Constraints: For quick opinion polls or pilot studies, the statistical rigour of stratified sampling may be unnecessary.
9. How does quota sampling compare to convenience sampling?
Both are non-probability methods, but quota sampling is more structured. Convenience sampling involves selecting participants who are simply easiest to reach, with no effort to make the sample representative. Quota sampling adds a layer of control by requiring the researcher to find a specific number of individuals with particular characteristics, ensuring some degree of representation for key subgroups, even if the selection within those groups is still one of convenience.
10. Is there a specific formula to determine the sample size for quota sampling?
No, there is no strict statistical formula for determining sample size in quota sampling, unlike in probability sampling. The size of the sample and the individual quotas are typically decided based on practical factors such as the research budget, timeline, and the analytical needs of the study. The goal is to have a large enough number in each subgroup to allow for meaningful observations, rather than to meet a statistically calculated threshold.
