What are the common quotas used to achieve a nationally representative sample?
We recommend using country-specific quotas to achieve a nationally representative sample. These quotas cover ideal sample proportions by gender, age, and location.
We have compiled information on the most commonly used quotas used to achieve a nationally representative sample for the following countries:
š¬š§ UK
| Gender | |
|---|---|
| Female | 50% |
| Male | 50% |
| Age | |
| 18-24 | 13% |
| 25-34 | 16% |
| 35-44 | 20% |
| 45-54 | 16% |
| 55-75 | 35% |
| Location | |
| South East | 14% |
| Greater London | 13% |
| North West | 11% |
| East Anglia | 9% |
| Midlands | 16% |
| South West | 8% |
| Yorkshire and Humberside | 8% |
| North East | 4% |
| Ireland | 3% |
| Scotland | 8% |
| Wales | 5% |
šŖšø Spain
| Gender | |
|---|---|
| Female | 50% |
| Male | 50% |
| Age | |
| 18-29 | 17% |
| 30-39 | 22% |
| 40-49 | 21% |
| 50-59 | 15% |
| 60-69 | 12% |
| 70+ | 13% |
| Location | |
| Andalucia | 19% |
| Aragon | 3% |
| Castilla y Leon | 5% |
| Castilla-La Mancha | 4% |
| Cataluna | 16% |
| Comunidad Valenciana | 11% |
| Euskal Autonomia Erkidegoa | 5% |
| Galicia | 6% |
| Islas Canarias | 5% |
| Madrid | 14% |
| Others | 13% |
šÆšµ Japan
| Gender | |
|---|---|
| Female | 51% |
| Male | 49% |
| Age | |
| 20-34 | 28% |
| 35-44 | 27% |
| 45-54 | 26% |
| 55-64 | 19% |
| Location | |
| Chubu | 17% |
| Chugoku | 6% |
| Hokkaido | 4% |
| Kanto | 33% |
| Kinki | 18% |
| Kyushu | 11% |
| Shikoku | 4% |
| Tohoku | 7% |
š¦šŗ Australia
| Gender | |
|---|---|
| Female | 49% |
| Male | 51% |
| Age | |
| 18-29 | 21% |
| 30-39 | 18% |
| 40-49 | 18% |
| 50-59 | 17% |
| 60-69 | 13% |
| 70+ | 13% |
| Location | |
| Sydney | 22% |
| NSW Regional | 12% |
| Melbourne | 20% |
| VIC Regional | 7% |
| Brisbane | 10% |
| QLD Regional | 11% |
| Perth | 8% |
| WA Regional | 2% |
| Adelaide | 6% |
| SA Regional | 2% |
šæš¦ South Africa
| Gender | |
|---|---|
| Female | 49% |
| Male | 51% |
| Age | |
| 21-34 | 39% |
| 35-44 | 17% |
| 45-54 | 15% |
| 55-64 | 20% |
| 65+ | 9% |
| Location | |
| Urban | 67% |
| Rural | 33% |
š¹š Thailand
| Gender | |
|---|---|
| Female | 50% |
| Male | 50% |
| Age | |
| 20-34 | 32% |
| 35-44 | 21% |
| 45-54 | 19% |
| 55+ | 28% |
| Location | |
| Bangkok | 78% |
| Pattaya-Chon Buri | 8% |
| Chiang Mai | 6% |
| Hat Yai-Songkhla | 5% |
| akhon Ratchasima | 3% |
š®š³ India*
| Gender | |
|---|---|
| Female | 48% |
| Male | 52% |
| Age | |
| 25-34 | 30% |
| 35-44 | 26% |
| 45-54 | 20% |
| 55+ | 24% |
| Location | |
| Mumbai | 12% |
| Delhi | 14% |
| Bangalore | 12% |
| Kolkata | 4% |
| Chennai | 14% |
| Ahmedabad | 2% |
| Hyderabad | 10% |
| Pune | 5% |
| Other | 27% |
*India has different legal drinking age in different states, we recommend to uniform qualifying age to start at 25 (which is the highest age for legal drinking in India) to be safe.
šØš³ China
| Gender | |
|---|---|
| Female | 49% |
| Male | 51% |
| Age | |
| 18-34 | 34% |
| 35-44 | 21% |
| 45-54 | 19% |
| 55+ | 26% |
| Location | |
| Tier 1 City | 10% |
| Tier 2 City | 9% |
| Tier 3 | 11% |
| Tier4 | 19% |
| Tier 5 | 51% |
š·š“ Romania
| Gender | |
|---|---|
| Female | 49% |
| Male | 51% |
| Age | |
| 18-34 | 9% |
| 35-44 | 27% |
| 45-54 | 36% |
| 55+ | 28% |
| Location | |
| Urban | 54% |
| Rural | 46% |
šš· Croatia
| Gender | |
|---|---|
| Female | 52% |
| Male | 48% |
| Age | |
| 18-34 | 29% |
| 35-44 | 19% |
| 45-54 | 18% |
| 55+ | 34% |
| Location | |
| Urban | 57% |
| Rural | 43% |
šØšæ Czech Republic
| Gender | |
|---|---|
| Female | 51% |
| Male | 49% |
| Age | |
| 18-34 | 30% |
| 35-44 | 19% |
| 45-54 | 18% |
| 55+ | 33% |
| Location | |
| Urban | 74% |
| Rural | 26% |
ššŗ Hungary
| Gender | |
|---|---|
| Female | 52% |
| Male | 48% |
| Age | |
| 18-34 | 30% |
| 35-44 | 18% |
| 45-54 | 18% |
| 55+ | 34% |
| Location | |
| Urban | 72% |
| Rural | 28% |