These sample size calculators take the pain out of calculating confidence intervals and sample sizes for quantitative research projects. Remember, we are always happy to provide advice on a no obligation basis, just call Eddy Graham on 01698 532021 or e-mail e.graham@ibp.eu.com.
MARGIN OF ERROR
The margin of error is the figure you usually see in opinion polls which would be expressed as something like +/-3%. For a random sample and a given confidence level (see below) it tells you that, if the sample answered in a given way, then if you had asked everyone in the survey population, the answer would fall within the parameters set by the margin of error (e.g. for a margin of error of 3, results would be +/-3%).
CONFIDENCE LEVEL
The confidence level, on the other hand, tells you how “sure” you can be. A 95% confidence level is usually quoted which would mean that you would be 95% certain that results for the whole population would lie within the confidence interval given by the sample.
Three main factors need to be taken into account:
Sample size: The larger the sample size then the smaller your margin of error. However, this is not a linear relationship; doubling the sample size does not halve the margin of error.
Percentage response: The margin of error also depends on the percentage of your sample that choose a given answer. If 90% of people respond in a given way, then the chances of an error are much lower than, if say, 50% of people respond that way. The rule of thumb is to calculate the margin of error on the worst case percentage (50%) and also to use this percentage to determine a general margin of error for a given sample size.
Population size: Population size only really matters when the sample is more than a few percent of the total population. For a population of, say 100,000 in a Council area the difference in margin of error between sample sizes of 500 and 1,000 would be quite small. If, however, the “population” is 2,000 Housing Association tenants, then this change in sample size does make more of a difference.
Technically, all of this only holds true if you have a genuinely random sample. This does not mean that you can just pick anyone to interview. On the contrary, a truly random sample requires everyone in the population to have an equal chance of being selected for interview. This means that you have to be very careful to make sure that your sample is not skewed for or against any particular segment of the population. One technique which is often used is quota sampling. This works on the assumption that if a sample has the breakdown characteristics of a random sample (e.g. in relation to age, sex, working status and so on) then it will have the characteristics of a random sample. This is the assumption made in many large-scale surveys and opinion polls.
Finally, in choosing your sample size or margin of error, it is important to apply common sense. The purpose of research is to inform decision making and the methodology to be adopted needs to be cost-effective and proportionate in relation to the decision to be made. If you are making low risk, low cost decisions then you may not even need any research; you certainly shouldn’t be incurring large costs. If however, you have to make difficult decisions about investment projects, or decisions that could have a big effect on how your organisation is perceived, then robust research, with appropriate sample sizes and well designed data gathering tools, is certainly recommended.
