# 2.1 Categorical And Numeric Measures

Data in Xynk is stored in a datasheet, in rows and columns. Each row contains the data for a single subject. Each column is called a measure. Measures come in two flavors: categorical measures and numeric measures. In the Measures source list and menus, categorical measures are denoted with a badge, while numeric measures have a badge.

Categorical measures are labels that specify attributes like a subject's name or ID, or the group they belong to, or a treatment condition. Because Xynk graphs and analyzes categorical data, the categorical measures are the independent factors that compose the X-axis of graphs. Typically, categorical data is made of strings, such as "Control", or "High-Fat Diet", or "Male", but a categorical measure can contain numbers, such as doses: "1 mg/kg", "3 mg/kg", "10 mg/kg".

[Examples of categorical measures]

Xynk also allows for Repeated Measures, which are a sequence of numeric measures grouped together: a repeated measure serves as both a categorical measure (the ordinal position of each measure) and numeric measures (the actual value recorded at each ordinal value). See the section on Repeated Measures below.

Numeric measures are, as the name implies, numbers -- the recorded values of observations for a subject under the conditions specified by the categorical measures. Numeric measures are the dependent measures the compose the Y-axis of graphs.

A dataset, and the subjects in the dataset, can have multiple categorical measures and multiple numeric measures. A dataset always has at least 2 categorical measures: a subject ID column, and a "Group" assignment column, but there can be more. A categorical measure will be specified as the independent factor in a one-way ANOVA; if 2 categorical measures are selected, they will be used as the factors in a 2-way ANOVA (and one factor will be plotted on the X-axis with the second factor as a subcategories within the first. There can be as many numeric columns as needed to include all the data from your experiment.