Sample size
After defining your study population and identifying your study design, you may ask how many people you need to include in your sample. The answer to this question depends on the nature of your research and the type of data you intend to collect.
If you want to work with qualitative data, and your main objective is to find out more about a particular problem but without seeking to generalise your findings to the entire study population, then the size of your sample does not matter. But if you want to work with quantitative data from a sample of people, and you want to use the findings to generalise to the wider population, then it is best to use as large a sample as possible, within available time and cost constraints. The logic is that the larger the sample, the more likely it is to be representative of the entire population, and therefore more reliable for generalising your findings to the population as a whole.
To work out the appropriate sample size for your study, there are many statistical procedures that can be used. It is not the objective of this Module to introduce these procedures and you do not need to know them at this level. Nevertheless, you may use the information in Table 15.1 as a rough guide to calculate an appropriate sample size for an investigation of a particular study population.
Table 15.1 Rough guide showing the required sample size for a particular size of study population.
| Study population size (number) | Required sample size (95% confidence level) |
|---|---|
| 30 | 28 |
| 100 | 80 |
| 500 | 217 |
| 1,000 | 278 |
| 5,000 | 357 |
| 10,000 | 370 |
| 50,000 | 381 |
| 100,000 | 383 |
| 1,000,000 | 384 |
The confidence level is an estimate of how certain you can be about the conclusions from your analysis, if the sample is the appropriate size. The recommended sample sizes given in Table 15.1 are those which give a confidence level of 95%, which means that in 95% of cases the sample you have selected will be large enough to be representative of the study population as a whole. Therefore, a 95% confidence level would enable you to have high confidence in the conclusions drawn from the results of your research on a sample of the specified size. This means that your sample would be representative and your findings could be generalised to the whole study population from which the sample was selected.
Supposing you have about 500 women who are past childbearing age in your kebele and you want to find out the average number of children they had during their childbearing years. You decide not to interview all 500 of these women, but to select a sample from this group to interview. Look at Table 15.1. Approximately how many women should you interview in order to be confident that the average number of children born to your sampled women is representative of the population as a whole?
You would need to interview around 217 women from the population of 500 to ensure that they are representative of the whole population of women past childbearing age.
In the final study session in this Module, you will find an extended case study that brings together many of the management, ethical and research issues you have met in this and earlier study sessions.