A must-have reference resource for qualitative management researchers, this dictionary contains over 90 entries covering the fundamentals of qualitative methodologies; covering both analysis and implementation. Each entry gives an introduction to the topic, lists the key relevant features, gives a worked example, a concise summary and a selection of further reading suggestions. It is suitable for researchers and academics who need a handy and quick point of reference.
The principle of sampling theory underpins a parametric technique for estimating confidence intervals. A confidence interval is a range of value of a sample statistic that is likely to contain an unknown population parameter at a given level of probability. The wider the confidence interval, the higher the confidence level. The normal distribution is used to calculate the limits if the population is normal and the standard deviation of the population is known. If normality cannot be assumed, a large sample size will ensure that the sampling distribution of the means is approximately normal (Oakshott, 1994).
Confidence intervals allow you to give an estimate of the reliability of your estimate by specifying some limits within which the true population value is expected to lie.