Let's Get Started With Quota Sampling

Quota Sampling

-Rajdeep Roy





What is quota sampling?

Quota sampling is defined as a non-probability sampling method in which researchers create a sample involving individuals that represent a population. Researchers choose these individuals according to specific traits or qualities. They decide and create quotas so that the market research samples can be useful in collecting data. These samples can be generalized to the entire population. The final subset will be decided only according to the interviewer’s or researcher’s knowledge of the population.


For example, a cigarette company wants to find out what age group prefers what brand of cigarettes in a particular city. He/she applies quotas on the age groups of 21-30, 31-40, 41-50, and 51+. From this information, the researcher gauges the smoking trend among the population of the city.


A number of comparisons between the results of quota and probability samples are summarized by Stephen and McCarthy (1958), who gave an excellent critique of the performance of both types of survey. The quota method seems likely to produce samples that are biased on characteristics such as income, education, and occupation, although it often agrees well with the probability samples on questions of opinion and and attitude.


Types of quota sampling:


Quota sampling can be of two kinds – controlled quota sampling and uncontrolled quota sampling. Here’s what they mean:


Controlled quota sampling:

Controlled quota sampling imposes restrictions on the researcher’s choice of samples. Here, the researcher is limited to the selection of samples.


Uncontrolled quota sampling:

Uncontrolled quota sampling does not impose any restrictions on the researcher’s choice of samples. Here, the researcher chooses sample members at will.



How to perform quota sampling:

Probability sampling techniques involve a significant amount of rules that the researcher needs to follow to form samples. But, since quota sampling is a non-probability sampling technique, there are no rules for formally creating samples. Usually, there are four steps to form a quota sample. Here are the steps:


  1. Divide the sample population into subgroups: With stratified sampling, the researcher bifurcates the entire population into mutually exhaustive subgroups, i.e., the elements of each of the subgroups becomes a part of only one of those subgroups. Here, the researcher applies random selection.
  2. Figure out the weightage of subgroups: The researcher evaluates the proportion in which the subgroups exist in the population. He/she maintains this proportion in the sample selected using this type of sampling method.
  3. For example, if 58% of the people who are interested in purchasing your Bluetooth headphones are between the age group of 25-35 years, your subgroups also should have the same percentages of people belonging to the respective age group.
  4. Select an appropriate sample size: In the third step, the researcher should select the sample size while maintaining the proportion evaluated in the previous step. If the population size is 500, the researcher can pick a sample of 50 elements. The sample chosen after following the first three steps should represent the target population.
  5. Conduct surveys according to the quotas defined: Make sure to stick to the predefined quotas to achieve actual actionable results. Don’t survey quotas that are full and focus on completing surveys for each quota.






How to perform "quota sampling" on a dataset in R:






Find out:

  • 5 abnormal, occurring on a Wednesday, with Humidity equal to 24.
  • 3 normal, occurring on a Workday, with Humidity less than 24.




library(samplingbook)
question1_data=subset(your_data,label==abnormal & Day=="Wednesday" & Humidity==24) 
sample_1=popsamp(5,question1_data)
sample_1
question2_data=subset(your_data,label==normal & Day=="Workday" & Humidity<24)
sample_2=popsamp(3,question2_data)
sample_2


Characteristics of quota sampling:

  1. Aims to get the best representation of respondents in the final sample.
  2. Quotas replicate the population of interest in a real sense.
  3. The estimates produced are more representative.
  4. The quality of quota samples vary.
  5. Saves research data collection time as the sample represents the population.
  6. Saves research costs if the quotas accurately represent the population.
  7. It monitors the number of types of individuals who take the survey.
  8. The researcher always divides the population into subgroups.
  9. The sample represents the entire population.
  10. Researchers use the sampling method to identify the traits of a specific group of people.


Applications of quota sampling:


Below are the instances where quota sampling is applied and used.

  • In situations where researchers have specific criteria for conducting research, it allows the selection of subgroups, due to which it becomes extremely convenient for researchers to obtain desired results. A trait or characteristic can be the filter for subgroup formation.
  • The researcher uses this method when he/she has time constraints. Applying quotas gives the researcher an idea of the whole population of interest in very little time.
  • Quotas are applied when the researcher is on a tight budget. Instead of researching a large population, the researcher saves money by using a few quotas to get the whole picture of the population.
  • Some research studies do not require pinpoint accuracy due to the nature of the research project. It is ideal for applying to quota sampling for these studies.




Example: Quotas have been set for gender only. Under the circumstances, it’s no surprise that the sample is representative of the population only in terms of gender, not in terms of race. Interviewers are only human;.




Quota Sampling over Stratified Random Sampling:





Example:


The student council at Cedar Valley Public School wants to gauge student opinion on the quality of their extracurricular activities. They decide to survey 100 of 1,000 students using the grade levels (7 to 12) as the sub-population.





The table below gives the number of students in each grade level.







Grade levelNumber of StudentsPercentage Of Students(%)Quota Of Students in sample of 100
71501515
82202222
91601616
101501515
112002020
121201212
Total1,000100100




The student council wants to make sure that the percentage of students in each grade level is reflected in the sample. The formula is:


Percentage of students in Grade 10
= (number of students ÷ number of students) x 100%
= (150 ÷ 1,000) x 100
= 15%


Since 15% of the school population is in Grade 10, 15% of the sample should contain Grade 10 students. Therefore, use the following formula to calculate the number of Grade 10 students that should be included in the sample:


Sample of Grade 10 students
= (15% of 100) x 100
= 0.15 x 100
= 15 students


The main difference between stratified sampling and quota sampling is that stratified sampling would select the students using a probability sampling method such as simple random sampling or systematic sampling. In quota sampling, no such technique is used. The 15 students might be selected by choosing the first 15 Grade 10 students to enter the school on a certain day, or by choosing 15 students from the first two rows of a particular classroom. Keep in mind that those students who arrive late or sit at the back of the class may hold different opinions from those who arrived earlier or sat in the front.


The main argument against quota sampling is that it does not meet the basic requirement of randomness. Some units may have no chance of selection or the chance of selection may be unknown. Therefore, the sample may be biased.


It is common, but not necessary, for quota samples to use random selection procedures at the beginning stages, much in the same way as probability sampling does. For instance, the first step in multi-stage sampling would be randomly selecting the geographic areas. The difference is in the selection of the units in the final stages of the process.


Advantages of quota sampling:

  • Saves time: Because of the involvement of a quota for sample creation, this sampling process is quick and straightforward.
  • Research convenience: By using quota sampling and appropriate research questions, interpreting information and responses to the survey is a much convenient process for a researcher.
  • Accurate representation of the population of interest: Researchers effectively represent a population using this sampling technique. There is no room for over-representation as this sampling technique helps researchers to study the population using specific quotas.
  • Saves money: The budget required for executing this sampling method is minimalistic.

To learn more, please follow the link below:

https://www.youtube.com/watch?v=saO1yLxd1p8

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