Quantitative data are directly collected as numbers and are usually subjected to statistical procedures such as calculating

the mean,

frequency distribution,

standard deviation etc. On higher levels of statistical analysis t-test, factor analysis, Analysis of variance, regression can also be conducted on the data. Quantitative data provides quantifiable and easy to understand results and can be analyzed in different ways. Quantitative data has four levels of measurement.

**Nominal** -Nominal refers to categorically discrete data. For example, name of a book, type of car you drive. Nominal sounds like name so it should be easy to remember.

__Ordinal__ - A set of data is said to be ordinal if the observations belonging to it can be ranked. It is possible to count and order but not measure ordinal data..

**Example:** T-shirt size (large, medium, small).

**Interval** - Measurements where the difference between values is measured by a fixed scale and is meaningful. Data is continuous and has a logical order and has a standard difference between values.

**Example: **Temperature, Money, Education (In years)

__Ratio__ - Ratio variables are numbers with some base value. Ratio responses will have order and spacing where multiplication makes sense too.

**Example:** Height, weight.

Once levels of measurement have been identified based on the data, appropriate statistical methods can then be used.