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Quota Sample


A quota sample a type of non-probability sample in which the researcher selects people according to some fixed quota. That is, units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied. For example, if you are a researcher conducting a national quota sample, you might need to know what proportion of the population is male and what proportion is female as well as what proportions of each gender fall into different age categories, race or ethnic categories, educational categories, etc. The researcher would then collect a sample with the same proportions as the national population.

In quota sampling, the researcher aims to represent the major characteristics of the population by sampling a proportional amount of each. Let’s say, for example, that you want to obtain a proportional quota sample of 100 people based on sex. First you would need to find out the proportion of the population that is men and the proportion that is women. If you found out the larger population is 40% women and 60% men, you would need a sample of 40 women and 60 men for a total of 100 respondents. You would start sampling and continue until you got those proportions and then you would stop. So, if you’ve already got 40 women for the sample, but not 60 men, you would continue to sample men and discard any legitimate women respondents that came along. You don’t need them because you have already “met your quota.” The difficulty here is that you have to decide in advance the specific characteristics on which you will base the quota. Will it be by gender, age, education race, religion, etc.?

Drawbacks Of Quota Sampling

Quota sampling has several drawbacks. First, the quota frame - or the proportions in each category - must be accurate. This is often difficult because it can be hard to find up-to-date information on certain topics. For example, U.S. Census data is often not published until well after the data was collected, making it possible for some things to have changed proportions between data collection and publication.

Second, the selection of sample elements within a given category of the quota frame may be biased even though is proportion of the population is accurately estimated. For instance, if a researcher set out to interview five people who met a complex set of characteristics, he or she might introduce bias into the sample by avoiding certain people or situations. If the interviewer avoided going to homes that looked particularly run-down or ones that occupied vicious dogs, for example, they are biasing the sample.


Let’s say that we want to understand more about the career goals of students at University X. In particular, we want to look at the differences in career goals between freshmen, sophomores, juniors, and seniors to examine how career goals might change over the course of a college education. University X has 20,000 students, which is our population. Next, we need to find out how our population of 20,000 students is distributed among the four class categories that we are interested in. If we discover that there are 6,000 freshmen students (30%), 5,000 sophomore students (25%), 5,000 junior students (25%), and 4,000 senior students (20%), this means that our sample must also meet these proportions. If we want to sample 1,000 students, this means that we must survey 300 freshmen, 250 sophomores, 250 juniors, and 200 seniors. We would then continue to randomly select these students for our final sample.


Babbie, E. (2001). The Practice of Social Research: 9th Edition. Belmont, CA: Wadsworth Thomson.

Trochim, M.K. (2006). Non-probability Sampling from the Research Methods Knowledge Base. http://www.socialresearchmethods.net/kb/sampnon.php

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