Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale. However, the ordered data may not have equal intervals. Ordinal variables have a clear ordering between the variables. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The data are merely classified into categories and cannot be arranged in any particular order. Ĭategorical or nominal variables are unordered. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.Ī hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales.
Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Quantitative or numerical data are subdivided into discrete and continuous measurements.
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