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Surveys are the most popular method to gather insights from the target population. Whether it is the fans of Disney movies or customers of a beauty brand, you cannot survey the entire population at the same time. So, the researchers found ways to create samples from the target population to gather representative data – i.e. the data that represents the entire target population.
One such popular method is quota sampling. So before you go about creating samples of your target market, you must understand what is quota sampling, its benefits, weakness, and how it works. And, this blog will help you learn all that.
We will start with the definition of quota sampling and how researchers create quota samples to conduct surveys.
By definition of quota sampling, it is a type of non-probability sampling method. In this sampling process, the elements from the population are chosen non-randomly, and all members of the population don’t have an equal chance of being a part of the sample group.
In this method of sampling, researchers typically use market research software to create two stages to acquire their quota sample.
1. First, they list relevant control characteristics and their distribution in the target population.
This is done to ensure that the composition of the selected sample group is representative of the composition of the target population (in regard to the listed control characteristics).
These “control characteristics” can be variables such as age, race, and sex. Researchers create these groups based on their own judgment.
2. The second stage is to select elements for the sample group based on the convenience and/or judgment of the researcher.
This is what differentiates this type of sampling from stratified sampling, as stratified sampling uses SRS (simple random sampling) or other probability sampling methods to choose elements for the sample group once the strata are divided.
To define quota sampling precisely, it is a two-stage non-probability sampling method that assigns quotas to the population in order to ensure that when elements of the population are selected, the sample group is representative of the population’s characteristics. After quotas are assigned, researchers choose elements from the subgroups using convenience or judgment.
Now that we have established the quota sampling definition, we will move on to its two types.
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With the definition of quota sampling out of the way, we can now focus on the two types. For market research tools, it can be divided into two broad categories.
The absence of random selection is what separates this sampling method from the rest. This is an ideal sampling method when the goal is to gather insights about certain characteristics of a particular sample group.
We will now look at the four simple steps to build your quota sample.
Let’s assume that a researcher wants to study the buying habits of the people in New York depending on their gender and employment status. In this example, gender and employment status will be the “relevant control characteristics”, using which the quotas will be decided.
Let’s consider the following factors for the quota sampling example.
Researchers will then use this information to reflect similar proportions of male/female and employed/unemployed in their sample group.
Let’s say a sample size of 100 people is decided upon. Researchers will use market research tools that have quotas to decide how many males and females are chosen in regard to their employment status.
Therefore, they may choose to include 60 females and 40 males, 10 of which are unemployed. These elements will be chosen by the researcher on the basis of convenience or judgment.
Here we have shared some examples of how quota sampling can be used in different research areas:
The next question that comes up often is when can you use this sampling method.
Let’s assume that a researcher wants to study the buying habits of the people in New York depending on their gender and employment status. In this example, gender and employment status will be the “relevant control characteristics”, using which the quotas will be decided.
Let’s consider the following factors for the quota sampling example.
Researchers will then use this information to reflect similar proportions of male/female and employed/unemployed in their sample group.
Let’s say a sample size of 100 people is decided upon. Researchers will use market research tools that have quotas to decide how many males and females are chosen in regard to their employment status.
Therefore, they may choose to include 60 females and 40 males, 10 of which are unemployed. These elements will be chosen by the researcher on the basis of convenience or judgment.
The next question that comes up often is when can you use this sampling method.
With this type of sampling, you can select subgroups, which makes it ideal for research. You can gather desired results from your surveys.
Here are five scenarios when you can use this type of sampling method to study the population.
It is a popular choice among researchers for many reasons. We have listed down some of these reasons that explain the advantages of the sampling method.
Affordability and ease are enough advantages to categorize it as an effective sampling method. However, there are some challenges that we will discuss next.
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Here are some limitations of this type of sampling you need to keep in mind when you conduct research.
When you want to conduct quota sampling for your research, it’s important to follow some best practices to maintain the integrity and ensure the reliability and quality of research data.
Let’s look into these in detail.
Identify the demographic that you intend to represent in your research sample. Define the target market segment based on the factors or characteristics.
Set proper quotas for each segment based on their proportion in the target population. Each quota should establish the importance of each sub-group and ensure representation.
It’s best also to introduce randomization within each quota in quota sampling techniques. This enables you to minimize bias as it ensures that the selection is not influenced by subjective judgment.
For reliable research results, monitor the sample recruitment process and track the process of data collection. Adjust the quotas when certain quotas fill up quickly or slowly to main balance and ensure generalization.
While demographic variables are the general choice in the quota sampling process, it’s best to use other variables as well. For example, in market research, use variables such as purchase history, product usage, etc. This will help you gather nuanced data.
Evaluate the representativeness of the sample by comparing its characteristics to those of the target population.
This sums up all about this sampling method. To recap, as the definition of quota sampling method goes, you use your personal judgment to select the final quota sample for the project. While this may create sampling error, you can always examine it using online survey software.
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