## Classifying Variables as Quantitative or Qualitative

Data in samples represent variables which are used to statistically analyze patterns.

For example, a sample of clinic patients would contain a list of their weight values. In this case weight is the variable.

### Quantitative or Qualitative

All variables are either quantitative or qualitative based on the type of values they assume.

#### Quantitative

These variables, weight for example, assume numeric values and are typically used in math operations such as using a person’s weight to produce a BMI index value.

Quantitative variables are further distinguished by how many values they assume – whether they are discrete or continuous.

Discrete quantitative variables, such as a patient’s number of limbs in an amputee clinic – each person can only have four limbs.

Continuous variables are the most common type of quantitative variables because they receive an unlimited number of numeric values.

#### Qualitative

Variables which receive non-numeric values, such as categories like race or religion are qualitative in nature.

Due to the nature of the values not being numeric, math operations are not performed on these values, but are usually aggregated within reports.

### Categorical or Numeric

Categorical variables describe a quality or characteristic of data and are either nominal or ordinal. Nominal (name) categorical variables contain values which may not be naturally organized by order, such as a list of peoples’ names. Ordinal (order) categorical variables contain values which naturally lend themselves to order, such as sizes or rankings.

Numeric variables contains number values which measure quantity and are discrete or continuous. Discrete numeric variables contain values with whole numbers only (quantity of apples) whereas continuous numeric variables reflect values as a fraction, such as someone’s age or height.