# A Different Kinds of data

## A.1 Learning Objectives

The properties of your data will dictate how it will be analysed.

- How you understand a variable dictates what you can do with it.
- Consider how you will handle variables
*before*doing statistics and data analysis.

## A.2 Variable Classes

**Independent Variables** are^{63} fixed parameters studied by the experimenter, e.g. genotypes, developmental stages, cell lines, growth conditions, etc.

**Dependent Variables** are^{64} what you have measured, and change according to the independent variables, e.g. expression levels, blood pressure, intensity, presence/absence of a substance, etc.

## A.3 Data Classes

**Categorical variables** are^{65} qualitative, with countable set of possible values that differ in kind, e.g. location, genotype, time interval, etc. There are two scales of categorical variables: *nominal* and *ordinal*. They are defined according to the properties in the following table:

Scale | Ordered | Example |
---|---|---|

Nominal | ✗ | Location, organ |

Ordinal | ✓ | Dose, copy number |

When categorical variables dictate how other variables, either categorical or continuous, are to be grouped, they are referred to as factors and their groups as levels.

**Continuous Variables** are^{66} quantitative and do not naturally fall into discrete categories, e.g. time, weight, and expression level, although they can be coerced into an ordinal variable.

In plotting, the dependent variable is usually on the Y-axis, the independent variable on the X-axis.↩︎

i.e. Dependent variables change according to the state of the independent variable.↩︎

You may also see this referred to as

*discrete*data, which refers to distinct, non-overlapping groups. Other times they are called*qualitative*, and as we’ll see on page**??**, they are also called*factors*.↩︎You may also see this referred to as

*quantitative*data, but we find the phrase continuous more meaningful and less confusing.↩︎