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:
🇬🇧 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% |
🇪🇸 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% |
🇯🇵 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% |
🇦🇺 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% |
🇿🇦 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% |
🇹🇠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% |
🇮🇳 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.
🇨🇳 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% |
🇷🇴 Romania
| Gender |
|
| Female |
49% |
| Male |
51% |
|
|
| Age |
|
| 18-34 |
9% |
| 35-44 |
27% |
| 45-54 |
36% |
| 55+ |
28% |
|
|
| Location |
|
| Urban |
54% |
| Rural |
46% |
ðŸ‡ðŸ‡· Croatia
| Gender |
|
| Female |
52% |
| Male |
48% |
|
|
| Age |
|
| 18-34 |
29% |
| 35-44 |
19% |
| 45-54 |
18% |
| 55+ |
34% |
|
|
| Location |
|
| Urban |
57% |
| Rural |
43% |
🇨🇿 Czech Republic
| Gender |
|
| Female |
51% |
| Male |
49% |
|
|
| Age |
|
| 18-34 |
30% |
| 35-44 |
19% |
| 45-54 |
18% |
| 55+ |
33% |
|
|
| Location |
|
| Urban |
74% |
| Rural |
26% |
ðŸ‡ðŸ‡º Hungary
| Gender |
|
| Female |
52% |
| Male |
48% |
|
|
| Age |
|
| 18-34 |
30% |
| 35-44 |
18% |
| 45-54 |
18% |
| 55+ |
34% |
|
|
| Location |
|
| Urban |
72% |
| Rural |
28% |