## Notes on data types

Nominal or category data are data that have simply been classified into groups, perhaps by colour or sex, that do not form a logical progression.

Ordinal and rank data are in categories that form a logical order, such as rankings or an assessment scale (eg the Beaufort wind-scale or the Richter scale for earthquakes)

Scale data are 'real' measurements, such as length or mass or simply number. Scale data are either 'discrete', if they derive from counting things, or 'continuous', if they are measurements on a scale such as length.

Parametric data must be scale data and should be normally distributed (or close to this). If a test involves more than one data set, the variances of the data sets should be similar.

Non-parametric data do not satisfy the parametric criteria, and may be category, ordinal or scale data. Tests appropriate to non-parametric data may also be used in the case of small sample sizes where the parametric criteria cannot be sustained.

Dependent and independent variables - where the value of one variable is controlled, at least partially, by the value of a second, the first variable is termed 'dependent' and the second 'independent'. In regression analysis, the dependent variable is usually denoted by 'y' and the independent variable as 'x'. Dependent variables are also known as 'test-' or 'response-' variables, and independent variables may be called 'predictor-', 'grouping-' or 'explanatory-' variables (or factors). In some types of analysis (but not covered here), there can be more than one independent variable.

Related and unrelated data - these terms are almost self-explanatory, but are important concepts in designing tests and analysing results. If data are related, an observation of one variable can be linked to a corresponding observation of a second variable. Obviously, this means that there must be the same number of observations in each set of data.