Schottler Consulting Social and Market Research Knowledge Centre

How many surveys do I need for my social or market research study?

In designing a sampling frame for a social or market research survey (i.e., an approach to sample selection), it is important to consider a range of factors. 

It is important to consider the volume of sample required to ensure that the selected sample provides an accurate estimate of the entire 'population' (i.e., All the respondents of interest) – if too few respondents are interviewed, 'random sampling error' may be high and imply a more biased estimate.

Tips for success in sampling for social and market research studies


The following tips are useful in determining the size of the sample you need for a survey or other data collection exercise:

  • Consider the groups or segments of interest – to ensure that there is sufficient data to permit reliable analysis of data following data collection, the sampling frame should include all groups or segments of interest

  • Use random sampling where possible – random sampling is the best technique to avoid sampling bias

  • If there are groups of interest, it is also possible to sample randomly within groups of interest (called stratified sampling) – e.g., surveying people who drink alcohol and those who do not. These are often referred to sampling 'quotas'

  • ​​​​The following table may be used to work out appropriate sample sizes for surveys. While many other more complex factors can influence sampling design, the table provides a 'ballpark' of sampling confidence intervals - or margins or error - for different sample sizes.

    It assumes an arbitrary population size of 5,000 (i.e., you're taking a sample of people from the total population that is estimated to around 5000 in total).

    It should be noted that population sizes in excess of thousands will generally not have a large effect on sample sizes required.

    So the following can also be considered as indicative for large populations of even 100,000 or more!

    This implies that there is a 95% chance that the given result is within +/- X% margin of error (confidence interval) of the obtained result.

    • N=357 achieves +/-5%​

    • N=536 achieves +/-4%

    • N=1622 achieves +/- 3%

  • Working out confidence intervals can also assist with research budgeting. 

    For example, if there is only a small improvement in sampling confidence intervals, it is arguably not worth spending additional budget on a larger sample, unless you need the sample for analysis. 

  • If there are important sub-groups in the population of interest, remember that the above sample sizes should also be applied to any segments of interest.

    For instance, for roughly +/-5% confidence within each segment, you could sample 536 each for 3 segments (N=1608 in total) and you would achieve roughly +/- 3% for the total sample.