Over decades, researchers are struggling to identify the correct sample size for their survey to receive statistically sound results. Statistically sound results offer the researcher’s confidence that the findings are real, dependable, and not occurred by chance.
What’s the sample size?
Market research and statistics use the term sample size frequently. It even pops up, when a large population survey is conducted. It is associated with the way study is performed on a massive group of people. Why does sampling size matter? When there is a large population involved in the survey, you are interested in receiving answers from everyone. Unfortunately, it is really impossible to gain results or answers from everyone. Therefore, you choose a random sample size, which represents the entire population.
Why sample size is crucial?
Sampling size matters because for successful research it is essential to receive statistically sound results. If the sample is extremely small then the group may include a disproportionate number of participants that are anomalies and outliers. It skews the results as you will never acquire a fair picture associated with the entire population. In case the sample is extremely big, then the research turns out to be time-consuming, costly, and complex. Even if the results are accurate the paybacks don’t compensate the expenses.
If you have worked your variables then calculate a sample size for any survey with this tool from OvationMR. If you have still not determined the sample size then consult the experienced team at OvationMR and they will guide you smoothly.
How to determine the right sample size?
There are a few factors that you will need to consider, which can impact your research. You can even gain an insight into the statistics involved, which will help to determine the right sample size. This increases the possibility of receiving statistically sound results.
Sample size variables
- Population size – Define the group that suits your study and who doesn’t. You will have an estimated range.
- The margin of error – It is also termed as ‘confidence interval’. It is a number that defines how much error is allowed and expressed in mean numbers. Set the difference between a mean number of population and a mean number of sample size.
- Confidence level – It defines the confidence level you desire for the real mean to fall within the set margin of error. Common confidence level set around 90%, 95% or 99%.
- Standard deviation – Define the number of received responses that will differ from one another and even the mean number. A standard deviation of .5 helps because the survey has yet to start and it ensures that your sample size is sufficient.
Calculate sample size
Use the online sample size calculator or pen and paper.
Find Z score
Z-score [standard score] is the numerical measurement, which defines the relationship of a value to the means. Z-score can be negative or positive. If it is negative the mean is below and vice versa. To apply the Z score, you need to know the standard deviation and mean.
For common confidence level the Z-scores are –
- 99% – 2.576
- 95% – 1.96
- 90% -1.645
For different confidence levels check the online Z-core table.
Use sample size formula
For large population or unknown population size using the formula –
Necessary sample size = [Z score] ² X StdDev X [1-StdDev] ÷ [margin of error] ²
For smaller populations, use the sample size calculator available on the OvationMR platform.