Quota sampling

In the case of quota sampling, the researcher starts with the knowledge of how the universe is divided by strata. The investigators are instructed simply to fill the cells, so that the sample obtained is indeed representative in terms of the cells. The procedures used in quota sampling make the choice of respondents the

Forms of non-probability sampling

Forms of non-probability sampling

Probability Quota Sampling

Convenience samples

Judgement samples


Convenience samples

Judgement samples


Types of non-probability sample responsibility of the interviewers. Unfortunately, this can lead to substantial bias that cannot be objectively measured. Wide use of quota sampling has been made in marketing research since it is relatively cost-effective compared with other methods. However, with the development of random sampling techniques, researchers have become more critical of the drawbacks of this method. Properly applied the method can be successful because it is possible to introduce representativeness by stratifying the quota sample by objective and known population characteristics such as age, sex, family status and socioeconomic group. Quota samples may be accurately constructed using classifications such as ACORN. This is because such classifications are based on objective distributions of statistical variations in demographic, housing and occupational factors.

The first step is to estimate the sizes of the various subclasses or strata in the population. This is usually done through reference to some outside source, for example, census of population data. The relevant strata to the study have to be specified. For instance, a person's age may be something that a researcher thinks is relevant to a particular study. As a consequence, age will have to be taken into account when drawing up the sample. For example, if it is found that in the population 25% of people are aged between 25 and 30, then the aim should be to make sure that 25% of the people in the sample fit into this age band. There are similar requirements for other age bands reflecting the proportions with which they appear in the population under study. Other factors as well as age might also be considered.



Group Type

01 - Affluent mature professionals, large houses Wealthy 02 - Affluent working families with mortgages Executives 03 - Villages with wealthy commuters 04 - Well-off managers, larger houses

Wealthy Achievers

05 - Older affluent professionals Affluent Gre s 06 - Farming communities

07 - Old people, detached houses

08 - Mature couples, smaller detached houses

09 - Larger families, prosperous suburbs Flourishing 10 - Well-off working families with mortgages Families 11 - Well-off managers, detached houses

12 - Large families and houses in rural areas

13 - Well-off professionals, larger houses and Prosperous converted flats

Professionals 14 - Older professionals in detached houses and apartments

Urban Prosperity

15 - Affluent urban professionals, flats Educated 16 - Prosperous young professionals, flats Urbanites 17 - Young educated workers, flats

18 - Multi-ethnic young, converted flats

19 - Suburban privately renting professionals

20 - Student flats and cosmopolitan sharers Aspiring 21 - Singles and sharers, multi-ethnic areas Singles 22 - Low-income singles, small rented flats 23 - Student terraces

Startin Out 24 - Young couples, flats and terraces

25 - White-collar singles/sharers, terraces

Comfortably Off

26 - Younger white-collar couples with mortgages

27 - Middle-income, home-owning areas Secure 28 - Working families with mortgages Families 29 - Mature families in suburban semis

30 - Established home-owning workers

31 - Home-owning Asian family areas

33 - Middle-income, older couples


34 - Lower-income people, semis

Prudent 35 - Elderly singles, purpose-built flats Pensioners 36 - Older people, flats


Group Type

Asian 37 - Crowded Asian terraces

Communities 38 - Low-income Asian families

Moderate Means

Post Industrial Families

Blue Collar Roots

39 - Skilled older family terraces

40 - Young family workers

41 - Skilled workers, semis and terraces

42 - Home owning, terraces

43 - Older rented terraces

44 - Low-income larger families, semis

45 - Older people, low income, small semis Struggling 46 - Low income, routine jobs, unemployment Families 47 - Low-rise terraced estates of poorly-off workers

48 - Low incomes, high unemployment, single parents

49 - Large families, many children, poorly educated

Hard Pressed

Burdened Singles


- Council flats, single elderly people

- Council terraces, unemployment, many singles

- Council flats, single parents, unemployment

High Rise Hardship


- Old people in high-rise flats

- Singles and single parents, high-rise estates

Inner City Adversity

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- Multi-ethnic purpose-built estates

- Multi-ethnic, crowded flats

Source: CACI4

Source: CACI4

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  • natalia
    How to take quota sampling for marketing research?
    6 years ago

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