Quota sampling is a non-probability sampling technique that ensures your sample includes the appropriate proportions of different subgroups within a population.
Unlike stratified sampling, which relies on random selection to divide the population into mutually exclusive subgroups, quota sampling involves intentionally selecting participants to match pre-determined quotas or targets. This approach does not use random selection, as is done in stratified sampling, but instead focuses on ensuring each subgroup is adequately represented.
In controlled quota sampling, participants are chosen to meet specific criteria related to the study’s objectives. This method is useful in market research, where sampling is used to compare the characteristics of different consumer subgroups. Researchers can set quotas based on age, gender, or income level to ensure the final sample accurately reflects the population’s diversity.
While quota sampling helps achieve a balanced sample, it can introduce sampling error since participants are not randomly selected. However, in cases where random sampling is not feasible or necessary, quota sampling offers an efficient way to gather diverse insights for your research project.
Example: Quota sampling
Suppose you are conducting a study on consumer preferences for various brands of smartphones. You want to ensure that your sample includes a representative mix of age groups and genders. Using quota sampling, you set quotas for each age group and gender based on their proportions in the population:
1. Age groups: 18-24 (20%), 25-34 (30%), 35-44 (25%), 45-54 (15%), 55+ (10%)
2. Gender: Male (45%), Female (55%)
You then collect data from participants who fit these quotas until each quota is filled. For example, if your sample size is 500, you would aim to survey 100 people aged 18-24, with 45 males and 55 females in that age group.
By using quota sampling, you ensure that your sample represents the key demographic groups in the population, making it easier to compare preferences across these groups.
When to Use Quota Sampling
Quota sampling is a valuable technique in qualitative and quantitative research when researchers want to focus on specific subgroups within a population or explore relationships between different subgroups. This method is helpful when a complete list of the population (sampling frame) is not available, as it allows researchers to obtain a sample that closely represents the population of interest.
Quota sampling results only reflect the characteristics of the sample itself and cannot be generalized to the entire population due to the non-probability nature of the sampling method. This means quota sampling is more susceptible to research bias than probability sampling techniques.
Despite its limitations, quota sampling can provide valuable insights into a population’s attitudes, behaviors, or circumstances, such as understanding respondents’ concerns about a particular issue. It is also useful when respondents are randomly encountered, such as through pop-up surveys, surveys embedded on websites, or street surveys.
Types of Quota Sampling
There are two main types of quota sampling:
- Proportional quota sampling
- Non-proportional quota sampling
Proportional quota sampling
In proportional quota sampling, you determine the sample size for each subgroup based on its proportional representation in the overall population. For example, if your population is 60% female and 40% male, your sample would reflect those proportions.
Example: Proportional Quota Sampling
Suppose you are a political scientist surveying to gauge voter sentiment before an upcoming election. You’ve decided to draw a sample of 1,000 registered voters in the state. To ensure your sample is demographically representative, you start by stratifying your population based on the following key variables:
- Gender (Male, Female)
- Age (18-34, 35-54, 55+)
- Race/Ethnicity (White, Black, Hispanic, Other)
- Geographic Region (Urban, Suburban, Rural)
By combining these factors, you end up with 36 distinct subgroups or strata (e.g., White females aged 18-34 living in urban areas, Hispanic males aged 55+ in rural regions, etc.). You then consult voter registration data to determine the proportions of these 36 subgroups within the overall state electorate. For example, your data shows that White females aged 35-54 make up 15% of registered voters statewide.
Using this population information, you set your quota targets to ensure that your sample of 1,000 voters matches these demographic proportions. So, in this case, you would aim to have 150 White females aged 35-54 participate in your survey. You continue this process, systematically recruiting participants from each of the 36 strata until you reach your predetermined quota targets for every subgroup.
Non-proportional quota sampling
Non-proportional quota sampling helps you set quota targets for each subgroup regardless of their actual representation in the population. This allows you to ensure adequate representation of smaller subgroups, even if they are a small minority in the broader population.
The choice between proportional and nonproportional quota sampling will depend on your specific research objectives and the characteristics of your target population.
Example: Non-Proportional Quota Sampling
Suppose you are researching how a clothing brand can serve customers better by offering inclusive sizes. Since you do not know the total number of customers or their shopping preferences, you conduct an online focus group. You aim for an equal percentage of clients who choose size S through L and size XL through 3X. You set a quota of 50 participants for each size group, regardless of the number of customers. You then collect data from participants who fit these size categories until each quota is filled.
The responses you gather from the XL-3X group can then be compared with those given by people who shop for S–L sizes. Comparing input from both groups can help you understand how to create products that offer all customers the same ease of access, even though the sample does not represent the actual proportions of each size group among the brand’s customers.
Example: Step-by-Step Guide to Quota Sampling
Quota sampling differs from probability sampling methods as it does not involve strict rules or a random selection process. However, there are general guidelines that researchers should follow to ensure a representative sample.
- Dividing the population into strata
- Determining a quota for each stratum
- Continuing recruitment until the quota for each stratum is met
Step 1: Divide the population into strata
In this step, you identify the key characteristics or subgroups (strata) relevant to your research question. These strata should be mutually exclusive and collectively exhaustive, meaning that each individual in the population belongs to only one stratum, and all strata together cover the entire population.
Example: Suppose you’re studying consumer preferences for a new product. You divide the population into strata based on age groups: 18-24, 25-34, 35-44, 45-54, and 55+.
Step 2: Determining a quota for each stratum
After dividing the population into strata, you need to determine the quota for each stratum. This quota represents the number of individuals you want to include in your sample from each stratum. Quotas can be set proportionally (based on the actual distribution of the subgroup in the population) or non-proportionally (based on the researcher’s judgment or the study’s requirements).
Example: Based on the age group strata, you set the following quotas: 20% of the sample from the 18-24 age group, 25% from the 25-34 age group, 25% from the 35-44 age group, 20% from the 45-54 age group, and 10% from the 55+ age group. If your total sample size is 500, you would aim to include 100 individuals from the 18-24 age group, 125 from the 25-34 age group, and so on.
Step 3: Continuing recruitment until the quota for each stratum is met
In the final step, you start collecting data from individuals who fit the criteria for each stratum. You continue recruiting participants until you have reached the predetermined quota for each stratum. Once all quotas are met, the sampling process is complete.
Example: You begin surveying consumers about their preferences for the new product. You keep track of the number of respondents in each age group and continue recruiting until you have reached the quota for each stratum. This means collecting data from 100 respondents in the 18-24 age group, 125 respondents in the 25-34 age group, 125 respondents in the 35-44 age group, 100 respondents in the 45-54 age group, and 50 respondents in the 55+ age group.
Once you have collected data from the required respondents in each age group, you can conclude the sampling process and begin analyzing the data.
Difference between Convenience Sampling and Quota Sampling
Convenience sampling is guided by the researcher’s ease of access and proximity to potential study participants. In this method, the researcher selects readily available units that are easy to recruit without considering their specific characteristics. Since the characteristics of the units are not known in advance, it is impossible to ensure that the resulting sample is representative of the target population.
Quota sampling requires the researcher to know the characteristics of the target population. The population is first divided into mutually exclusive subgroups or quotas based on relevant traits such as age, gender, income level, etc. The researcher then sets target numbers or quotas for each subgroup and continues recruiting participants until those quotas are filled.
Advantages and Disadvantages of Quota Sampling
Here are the advantages and disadvantages of quota sampling:
| Advantages | Disadvantages |
| Quota sampling is less expensive and time-consuming than probability sampling methods, as it does not require a complete sampling frame or a random selection process. | Quota sampling is a non-probability sampling method, meaning that the sample is not randomly selected, and not every individual in the population has an equal chance of being included. This can introduce bias into the sample. |
| It allows researchers to ensure that specific subgroups or strata within the population are adequately represented in the sample, enabling comparisons between these subgroups. | The non-random nature of quota sampling means that, unlike probability sampling methods, the results cannot be generalized to the entire population with a known confidence level. |
| Quota sampling can be useful when a complete sampling frame is unavailable or when the population is hard to reach, as it relies on the researcher’s ability to identify and recruit participants who fit the desired criteria. | This flexible sampling method can be adapted to suit different research questions and population characteristics. The researcher can define the relevant strata and quotas based on the study’s requirements. |
| This sampling method is flexible and can be adapted to suit different research questions and population characteristics. The researcher can define the relevant strata and quotas based on the study’s requirements. | Quota sampling may not capture the full diversity within each stratum, as the researcher may only select participants who are easily accessible or willing to participate, leading to an underrepresentation of hard-to-reach individuals. |
| Quota sampling can help reduce non-response bias. The researcher can continue recruiting participants until the desired quota for each stratum is met, ensuring a sufficient sample size. | If the chosen strata or quotas do not accurately reflect the population’s characteristics, the sample may be skewed, and the results may not represent the target population. |
Application of Quota Sampling Research Methods
Quota sampling is a non-probability sampling method that divides a population into specific subgroups and selects participants to fill a predetermined quota for each group.
Unlike stratified random sampling, quota sampling doesn’t use random selection, making it more efficient when strict randomness is not essential for the research. It is applied in the following instances:
- Time Constraints: Researchers use the quota sampling method when they have limited time. By applying quotas, they can get an idea of the population of interest quickly without needing to survey a large number of people.
- Budget Limitations: Quota sampling is often used when the researcher has a tight budget. Instead of surveying a large population, a few quotas are selected to save costs while still gaining insights into the overall population.
- No Need for a Sampling Frame: Since quota sampling doesn’t require a sampling frame or strict random selection, researchers can gather data even when they lack complete population information, making it ideal for projects with limited resources.
- Quick Representation of Subgroups: This type of sampling method is applied when the researcher needs quick representation of subgroups. Quotas allow them to capture diverse characteristics of the population without the need for random selection.
- Online Surveys: Quota sampling is used in online surveys to balance the representation of various demographic groups, ensuring the sample reflects the population without the need for complex sampling techniques.