What does it do?
The sample size calculator allows you to calculate the required responding sample size, standard error, RSE, and a confidence interval (95% or 99%) for a proportion estimate, using just one of these criteria as an input. For example, if you know the standard error you need to meet precision requirements of your estimate, you can find out the responding sample size required to achieve that; if you know the likely size of the responding sample you can estimate the standard error of your estimate, and a confidence interval for it.
It is recommended that the level of precision be set to allow the survey to achieve the desired outputs. The level of precision should be set in conjunction with the users of the data. You should not set the accuracy levels too high, as you will incur higher costs (due to a larger sample size) and place additional burden on the community. You should also not set the accuracy levels too low, as your data may not be appropriate for your users.
The sample size calculator assumes simple random sampling. The results generated here are intended only as a guide and should only be used as such. They are by no means the definitive "rule" about the size of a sample.
How do I use it?
Simply follow the steps outlined below.
What do the categories mean?
Confidence Level
This is the level of certainty with which you will estimate the true population value. You can select either 95% or 99%, meaning that there are 95 (or 99) chances out of 100 that the true value of the estimate is within the confidence interval.
Population Size
This is the total number of individuals or groups about which information is required. This option can be left blank, in which case the population size is assumed to be very large.
The target population is the population about which information is to be sought. The target population is also known as the scope of the survey i.e. the population that the survey is aimed at. For a sample survey, a sample is taken from the population.
Proportion
This specifies the expected proportion of the population to have the attribute that you are estimating from your survey. This assists in calculating the estimated standard errors that are appropriate for your situation. You can get the proportion from previous cycles of the survey or by an educated guess. If this proportion is unknown, it should be set to 0.5 (i.e. 50%), as this produces a conservative estimate of variance.
For example, if you want to estimate how many customers were satisfied with your service, and you have data from last year saying that 50% of customers were satisfied, then you would enter 0.5.
Confidence Interval
The confidence interval allows you to specify the desired level of accuracy of the estimate. The confidence interval value is expressed as a proportion, meaning that if you want the result to be accurate within 5 percentage points, then you should enter 0.05. You can specify an interval from 0 to 0.5. The narrower the interval the more precise the estimate.
Confidence Interval: Upper and Lower
These are the upper and lower bounds of the confidence interval, as determined by the specified interval. For example, if the is proportion os 0.5 and the specified confidence Interval is 0.05, then the upper and lower bounds will be 0.5 +/- 0.05, which gives you a confidence interval from 0.45 to 0.55.
Standard Error
This is a measure of sampling error that indicates the degree to which an estimate may vary from the true value. The standard error is expressed in the same units as the estimate (in the case of this calculator it is a proportion). Higher standard errors indicate more variation in the estimate.
Relative Standard Error (RSE)
This is the standard error expressed as a percentage of the estimate. The RSE is a useful measure of accuracy, as it gives an indication of the percentage errors likely to have occurred due to sampling. For example, if the proportion is 0.5 and the standard error is 0.05 then the RSE will be 10%.
Sample Size
The sample size refers to the number of individuals or groups required to respond (not just the number approached) to achieve the required level of accuracy (specified by entering either the confidence or error required). For example, if your required sample size from the calculator is 100 and you expect only 10% of your sample to respond, then you will need to approach 1000 units in order to achieve the sample size of 100.
What do the Results mean?
Entering the Confidence Level, Proportion, and Confidence Interval into the Calculator to determine the required sample size:
To be 95% confident that the true value of the estimate will be within 5 percentage points of 0.5, (that is, between the values of 0.45 and 0.55), the required sample size is 385. This is the number of actual responses needed to achieve the stated level of accuracy. You need to take into account any expected non-response when calculating the number of individuals or groups that you will approach for the survey. The standard error of 0.026 on the estimate of 0.5 produces an RSE of 5.1%.
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For more examples of how to use the Sample Size Calculator, see Sample Size Calculator Examples.
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