Quota sampling is an effective method of research. When a researcher gathers data from a population, they can select two subgroups to use for the research. These two subgroups will provide insights into the population.

The researcher can decide to use education, gender, or social-economical standing to carry out the research.

Sometimes a researcher might face temptation when it comes to selecting participants to attempt the survey. Quota sampling uses the reliability of the researcher because it is a non-probability test.

Now since the reliability of a test depends on the researcher, if the researcher selects random individuals to complete a form or employ the services of familiar individuals so as to meet the allocated deadline, the researcher is at risk of research bias.

We are going to carefully explore the concept of quota sampling in this article. First, let us look at what quota sampling means.

Quota sampling is a method where a researcher selects a sample group to represent some specific characteristics of the population.

The researcher chose this sample group to represent the entire population so that they can get the desired result. One thing to note is that quota sampling does not depend on probability because it can be controlled. Untill the selected quarter is derived, a researcher does not use randomization to obtain data from the sample group.

Also, a researcher selects the sample size and ensures they have traits peculiar to the entire population. This is so that at the end of the research, the outcome from the sample size can be generalized to the population.

Another thing a researcher must understand is that proportion must be accurate in quota sampling. The researcher also has an option of setting a quota sampling percentage higher or lower than what is obtained in the population's proportion. If the researcher feels adding or reducing from a population's proportion will represent the population better, the researcher is at liberty to do so.

We can divide quota sampling into two groups;

**1. Controlled quota sampling:** In this type of quota sampling the researcher is limited in the selection of the sample group participants. This means the researcher is restricted.

**2. Uncontrolled quota sampling:** in uncontrolled quota sampling the researcher is free to select the participants of a sample group depending on his knowledge of the population, or how he deems fit.

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Before you undergo quota sampling, understand that it does not constitute following many formal rules, unlike other probability sampling methods which require a number of rules a researcher must follow before developing samples.

There are four steps to follow when creating a quota sampling;

Divide the total population into two equal subgroups. The characteristics of each subgroup will be limited to that group. What this means is that a subgroup can be the treatment group while the other group will be the control group. You can then use a random method of selection.

Once you have divided your population into two subgroups, find out the proportion of each subgroup in the entire population and maintain this percentage.

Let us look at this example. If 62% of people show interest in purchasing headphones from your company, and they're between the age of 30 to 40 years, your subgroup should represent the same percentage of people within this same age group.

Bear in mind the evaluated proportions in the above steps and ensure to maintain them while selecting your sample size. For example, if your population is 2000, you can have a sample size of 200. The important thing is for your sample size to represent the population.

If you want accurate results and error-free research, focus on analyzing the predicted quota to gather your results. Also ensure all surveys are completed.

Read:What is Stratified Sampling? Advantages, Examples, Definition, Types

Here are the top characteristics of the quota sampling method;

- The primary objective of the quota sampling method is to select participants that truly represent the sample population.
- Quota sampling varies in quality
- Quota sampling saves research time because the selected sample group represents the population.
- Quota sampling aims to represent the population realistically.
- Quota sampling is used by researchers to determine the characteristics of a specific group.
- Another characteristic of quota sampling is that it saves research costs once a researcher can select the appropriate sample group that represents the entire population.
- Quota sampling analyzes in numbers, the types of people that partake in the tests or survey.
- Another characteristic of quota sampling is that the researcher always segregates the population into subgroups.
- The analysis and prediction from quota sampling usually represent the entire population accurately.

- One advantage of quota sampling is it saves time. It's the ideal choice for gathering primary data within a limited time.
- Quota sampling reduces cost. This is because less time is used to gather data.
- Quota sampling can be used in the absence of sampling frames. When the original set of data that samples can be drawn from is absent, the best choice is to apply the quota sampling method.
- Another advantage of quota sampling is that the researcher can conveniently analyze and interpret the responses to the test or survey. This is because the right questions are presented to the right sample group.

- One disadvantage of the quota sampling method is that it is risky to project the research result to the whole population because you cannot calculate the sampling error of the test from one quota. This is because quota sampling is not a probability sampling method.
- The quota sampling mostly accurately represents the characteristic of the population. However, there might be population feature inaccuracy in the total sample group.
- If the researcher is not experienced or not competent the quality of the method may suffer bias. This will make the results of the sampling inaccurate

Read:Undercoverage Bias: Definition, Examples in Survey Research

To begin with, when a researcher has a particular standard for conducting a study, it is best to use quota sampling because it allows the researcher to select subgroups. This makes it extremely easy for the researcher to obtain desired outcomes from the study.

Also, characteristics or traits to be measured can be sieved out from the population, and integrated into the subgroups.

Quota sampling is used when a researcher wants to conduct a study, has limited time, and is also trying to save costs. Using the quota sampling method gives the researcher an overview of the entire population in less time.

Read:11 Retrospective vs Prospective Cohort Study Differences

Another thing is quota sampling allows the researcher to save costs because instead of conducting research on the entire population, the researcher can use a few quarters to understand the total population thereby saving lots of money.

Quota sampling is also used when a researcher does not need detailed accuracy from the outcome of a survey or test.

It should be noted that a researcher should be familiar with the population and the aim of the test should be well understood so that the researcher can choose relevant sample groups.

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For example, let us assume a researcher is tasked with the responsibility of evaluating the impact of cross-cultural diversity in improving effectiveness, among employees in 10 pharmaceutical industries in London, with a population of 1000.

Once the researcher understands the aim of the study, the next thing is to assess how diversity has improved the effectiveness of employees with a focus on gender differences among these employees.

**Explore: ****21 Diversity & Inclusion Survey Questions**

To achieve this, a researcher can apply quota sampling using the following steps:

- Segregate the population into particular subgroups that can represent the entire population. Since the focus is on cross-cultural diversity, the population should be divided based on cultural background. For example, divide the population into four; Asian, African, European, and others.
- Next, select representatives from each subgroup. You can select 50 participants each. That way you have a final sample size of 200 participants to be used for your study.
- Once you have selected your participants, determine the conditions to be met and the quota for each group.

Note that it is important that all genders are represented in your sample group equally. To achieve this, you can divide your group into 25 females and 25 males in each group. This is important for your research.

Once this is done and your sample group represents your study population, then you can perform your research.

We are going to consider these examples to have a clearer understanding of quota sampling.

**Example 1:**

A researcher wants to find out the smartphone that most individuals prefer to buy and use. This researcher wants to survey ten countries in Africa. To carry out this research, the researcher considers a sample size of 5,000 participants.

Now we are going to look at how the researcher can segregate the population by quotas.

- The researcher can divide into quotas using gender. Here, they will divide the 5,000 participants into an equal group of 2500 females and 2500 males.
- The researcher can further divide using the age of the participants. That will be 1000 participants each representing ages 16 to 20, 21 to 30, 31 to 40, 41 to 50, and 50 and above.
- The researcher can use socioeconomic status such as employment to further divide the participants. This can be in the ratio of 3500 employed participants and 1500 unemployed participants. The researcher can also use a subset quota to segregate the unemployed participant. So that 500 out of the 1500 unemployed participants are fresh graduates.
- The researcher can also use location to divide the participants. Let's say 500 participants per country.

If you look at the example where the researcher used a sample size of 3500 employed and 1500 unemployed participants, you will note that it is not necessarily compulsory for the researcher to divide quota evenly.

**Example 2:**

Let us also consider this example.

A brewery company decides to find out the age group that prefers a particular brand of wine in a specific state.

To determine this the researcher uses a quota sampling method on the following age groups.

Age groups 21 to 30, 31 to 40, 41 to 50, and above 50.

The researcher will analyze the results from the findings and this will provide insights for the company on the drinking trend in the state population.

Note that in these two examples not the whole population who was involved in the survey rather a sample size was used to represent the entire population in its true characteristics. This is what the quota system means. A sample group is used to determine the value of the whole population.

In this article, we have looked at the concept of quota sampling and reached a conclusion that quota sampling is better applied when a researcher is trying to save costs and has limited time to conduct a study.

It should also be noted that quota sampling is best used to represent a large population. The researcher can select a sample group that has relevant characteristics obtainable in the population, use them for the survey and generalize the finding to the population.

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