“Quotas” are selecting fixed numbers of units in each of a number of categories.
Definition: A data collection method designed to select sample units in a block of a predetermined size.
In simple words, in a Quota sample, quotas are set up according to some/ specified characteristics.
It is not based on random selection. Instead, respondents who fit into predetermined categories ('quota controls') are found by interviewers until their quotas are filled.
Let us do an activity. You are given a population. You have to decide the quotas for you sample. For this survey, the quota controls to be used are sex and age (young, middle, and old).
A recent census has reported that there are:
5252 males
5789 females
2940 young
3921 middle aged
4180 old
In simple words, in a Quota sample, quotas are set up according to some/ specified characteristics.
It is not based on random selection. Instead, respondents who fit into predetermined categories ('quota controls') are found by interviewers until their quotas are filled.
Let us do an activity. You are given a population. You have to decide the quotas for you sample. For this survey, the quota controls to be used are sex and age (young, middle, and old).
A recent census has reported that there are:
5252 males
5789 females
2940 young
3921 middle aged
4180 old
in the town. You should choose the numbers in your sample in each category so that they are approximately representative of the town as a whole.
How many men do you want in your sample? How many women do you want in your sample? How many young people do you want in your sample? How many middle-aged people do you want in your sample? How many older people do you want in your sample?
How many men do you want in your sample? How many women do you want in your sample? How many young people do you want in your sample? How many middle-aged people do you want in your sample? How many older people do you want in your sample?
When you decide the approximate number of quotas in each category, that means you have already done your quota sampling.
When using this strategy, researchers identify important characteristics that they already know the target population possesses, and then they select the nonrandom (and therefore biased) sample in such a way as to make it correspond to the population with regard to these known characteristics.
For example, we might get a quota sample of Indian teenagers in a city by consulting census information and discovering what percentage of teenagers in that city is of each gender, what percentage belongs to each of the various races, and what percentage lives in each of several different neighborhoods. Based on this information, we would set quotas even before we set out to conduct our survey, determining that we would get a certain number of males, a certain number of females, a certain number of engineering students, of arts students, and so forth. When conducting the survey, we would use these quotas to set the limit on how many persons possessing each characteristic we would include in our survey.
Although it is desirable to set quotas before we select the sample, it is also possible to use quota sampling strategies retrospectively. For example, an organization with a small budget may be interested in knowing the attitudes of Indian college students regarding drug and alcohol use. Realizing that students are likely to react to questions by giving socially desirable answers, this organization might hire a researcher at a nearby university who is known for her ability to establish rapport and obtain frank answers from students. Because of time and travel restrictions, this researcher would have to collect her data from respondents at or near her own university. She might obtain detailed responses to a wide variety of questions from all 200 of the students in her classes, which all are required to take. Can the organization use these results to generalize about "Indian college students"? A further examination indicates these students had CET scores typical of Indian college students, and that there were percentages of south Indians, north Indians, males, females, old students, young students, rich students, poor students, arts majors, engineering majors, etc. comparable with the percentages known to be typical of the rest of the country. The researcher also notes that two of her many questions overlapped almost exactly with those asked by a nationally prominent survey organization, and the responses of her students were almost identical to those. At this point, the organization has good reason to believe that these results can be generalized. Their confidence cannot be as great as if they had conducted a random survey with an equally good interviewer, but they are more confident than if they had sent their interviewer over to the local area to interview students or if they had conducted a random survey in a manner very likely to obtain reactive, false responses.
The flaw in this after-the-fact quota sampling is that the demographics of the sample may indeed reveal obvious biases with regard to the target characteristics. Then the researcher is left with nothing more than a limitation that can be stated but no longer corrected. For instance, in the preceding example, what would the organization do if the researcher reported that her group included more of South Indian students and engineering majors? This would be a difficult problem with after-the-fact quota sampling. Preplanned quota sampling is more likely to minimize differences. In the preceding example, the problems would be minimized by selecting fewer South Indian students or engineering majors for the sample. The retrospective strategy can increase our confidence in nonrandom samples when the subjects meet the quotas and caution us regarding the nature of biases when the subjects do not meet the quotas.
When using this strategy, researchers identify important characteristics that they already know the target population possesses, and then they select the nonrandom (and therefore biased) sample in such a way as to make it correspond to the population with regard to these known characteristics.
For example, we might get a quota sample of Indian teenagers in a city by consulting census information and discovering what percentage of teenagers in that city is of each gender, what percentage belongs to each of the various races, and what percentage lives in each of several different neighborhoods. Based on this information, we would set quotas even before we set out to conduct our survey, determining that we would get a certain number of males, a certain number of females, a certain number of engineering students, of arts students, and so forth. When conducting the survey, we would use these quotas to set the limit on how many persons possessing each characteristic we would include in our survey.
Although it is desirable to set quotas before we select the sample, it is also possible to use quota sampling strategies retrospectively. For example, an organization with a small budget may be interested in knowing the attitudes of Indian college students regarding drug and alcohol use. Realizing that students are likely to react to questions by giving socially desirable answers, this organization might hire a researcher at a nearby university who is known for her ability to establish rapport and obtain frank answers from students. Because of time and travel restrictions, this researcher would have to collect her data from respondents at or near her own university. She might obtain detailed responses to a wide variety of questions from all 200 of the students in her classes, which all are required to take. Can the organization use these results to generalize about "Indian college students"? A further examination indicates these students had CET scores typical of Indian college students, and that there were percentages of south Indians, north Indians, males, females, old students, young students, rich students, poor students, arts majors, engineering majors, etc. comparable with the percentages known to be typical of the rest of the country. The researcher also notes that two of her many questions overlapped almost exactly with those asked by a nationally prominent survey organization, and the responses of her students were almost identical to those. At this point, the organization has good reason to believe that these results can be generalized. Their confidence cannot be as great as if they had conducted a random survey with an equally good interviewer, but they are more confident than if they had sent their interviewer over to the local area to interview students or if they had conducted a random survey in a manner very likely to obtain reactive, false responses.
The flaw in this after-the-fact quota sampling is that the demographics of the sample may indeed reveal obvious biases with regard to the target characteristics. Then the researcher is left with nothing more than a limitation that can be stated but no longer corrected. For instance, in the preceding example, what would the organization do if the researcher reported that her group included more of South Indian students and engineering majors? This would be a difficult problem with after-the-fact quota sampling. Preplanned quota sampling is more likely to minimize differences. In the preceding example, the problems would be minimized by selecting fewer South Indian students or engineering majors for the sample. The retrospective strategy can increase our confidence in nonrandom samples when the subjects meet the quotas and caution us regarding the nature of biases when the subjects do not meet the quotas.
Characteristic:
Within the quotas, the selection of sample items depends exclusively on personal judgment.
E. g. In a radio listening survey, the aim is to interview 500 people living in a certain area and that out of every 100 people interviewed 60 are to be housewives, 25 farmers and 15 children under the age 15. Within these quotas the interviewer is free to select the people interviewed.
Advantage:
1. It is less costly. The cost per person interviewed is relatively small.
2. It is administratively easy.
3. It is quick to do.
4. It does not need any sampling frame.
5. Can be used when random sampling is impossible.
Within the quotas, the selection of sample items depends exclusively on personal judgment.
E. g. In a radio listening survey, the aim is to interview 500 people living in a certain area and that out of every 100 people interviewed 60 are to be housewives, 25 farmers and 15 children under the age 15. Within these quotas the interviewer is free to select the people interviewed.
Advantage:
1. It is less costly. The cost per person interviewed is relatively small.
2. It is administratively easy.
3. It is quick to do.
4. It does not need any sampling frame.
5. Can be used when random sampling is impossible.
Disadvantages:
1. Since the samples are not randomly selected, the sample selected under this technique may not be true representative of the universe.
2. Within quota the sampling may be unrepresentative (eg all young, attractive females)
3. Control over fieldwork is difficult. Hence, the results may be biased because of the personal beliefs and prejudices of the investigator in the selection of the units under study.
1. Since the samples are not randomly selected, the sample selected under this technique may not be true representative of the universe.
2. Within quota the sampling may be unrepresentative (eg all young, attractive females)
3. Control over fieldwork is difficult. Hence, the results may be biased because of the personal beliefs and prejudices of the investigator in the selection of the units under study.
Limitations:
1. There are numerous opportunities or biases which may invalidate the results.
2. If a person refuses to respond, the interviewer simply selects someone else.
Because of the risk of personal prejudice and bias entering the process of selection, the quota sampling is not widely used in practical work.
3. Non-random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.
When to use: Quota sampling is often used in public opinion studies. It occasionally provides satisfactory results if the interviewers are carefully trained and if they follow their instructions closely. If done well, quota sampling can lead to strong inferences.
Activity:
You want to study opinions of people about the educational programs that are broadcasted on television. What type of samples would you think would be important to include? Form quotas that you feel appropriate for conducting this research.
1. There are numerous opportunities or biases which may invalidate the results.
2. If a person refuses to respond, the interviewer simply selects someone else.
Because of the risk of personal prejudice and bias entering the process of selection, the quota sampling is not widely used in practical work.
3. Non-random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.
When to use: Quota sampling is often used in public opinion studies. It occasionally provides satisfactory results if the interviewers are carefully trained and if they follow their instructions closely. If done well, quota sampling can lead to strong inferences.
Activity:
You want to study opinions of people about the educational programs that are broadcasted on television. What type of samples would you think would be important to include? Form quotas that you feel appropriate for conducting this research.
Resources:
http://education.calumet.purdue.edu/vockell/research/chapter8.htm
http://instruct.uwo.ca/fim-lis/504/504sam.htm
http://mot.vuse.vanderbilt.edu/mt322/IntroSam.htm
Sharma Jai Narain (2007), Research methodology: the discipline and its dimensions,
New Delhi Deep and Deep Publications.
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