What is Quota sampling?
Quota sampling is a non‑random sampling method that sets targets (quotas) for subgroups you want in your survey so the final sample matches those proportions. It helps ensure voices from key groups — for example language, age or neighbourhood — are included even when you can't run a full probability sample.
In quota sampling you decide which population characteristics matter for your study (called strata), set how many responses you need from each group, and then collect responses until each quota is filled. For example, you might aim for 200 responses split evenly across four language groups or by age bands. Unlike random or probability sampling, quota sampling does not select every respondent by chance — interviewers or recruitment methods fill quotas directly — so it is quicker and cheaper but can be more vulnerable to selection bias. Quota sampling is commonly used when you need a practical, cost‑effective way to ensure representation from specific subgroups rather than to produce statistically precise population estimates.
Usage example
A school running a parent feedback survey needs answers from families who speak English, Polish, and Arabic. Instead of waiting for whoever responds, the administrator sets quotas: 100 English responses, 50 Polish, and 50 Arabic. They stop collecting responses for each language once the quota is met so the final dataset includes voices from all three communities.
Practical application
Quota sampling matters because it helps you hear from underrepresented groups without the time and cost of a full probability survey. For Hearo users — schools, councils and charities — quotas ensure consultations and feedback include communities that might otherwise be drowned out (for example smaller language groups). It’s especially useful when you need balanced subgroup comparisons, want to prioritise inclusion, or have limited resources. Remember: quota samples give good directional insight and comparative analysis across groups, but they are not a substitute for probability sampling when you need precise, generalisable population estimates with calculable margins of error.
FAQ
How is quota sampling different from stratified random sampling?
Both divide the population into subgroups (strata), but stratified random sampling selects respondents at random within each stratum, giving you probability‑based results and valid statistical inference. Quota sampling fills each stratum by recruiting respondents until the target is reached, without random selection, so it’s faster and cheaper but more prone to selection bias.
When should I use quota sampling?
Use quota sampling when you need to ensure participation from specific groups (languages, age groups, neighbourhoods) quickly and affordably — for example consultations, user feedback, or pilot research. It’s ideal for improving inclusion and comparing groups, but not for producing precise population estimates required for formal statistical inference.
Can quota sampling produce representative results?
Quota sampling can approximate representation for the characteristics you set quotas on, but it doesn’t guarantee a fully representative sample of the whole population. Because respondents aren’t chosen randomly, results can be affected by who is easiest to reach or most willing to respond. Treat findings as indicative rather than strictly generalisable.
How should I choose quotas (sizes and categories)?
Choose quotas based on the purpose of your survey and available population information. Common choices are language, age, gender, location or service usage. Balance practical constraints (how many responses you can realistically collect) with the need for meaningful subgroup comparisons. If you have population benchmarks (e.g., school enrolment by language), use those proportions to set quotas; otherwise choose sizes that will give you enough responses to compare groups sensibly.