Statistics – Data:

Datum is the singular form of the noun where as Data is plural form which has been since 20^th century. Data can be categorized as either numeric or nonnumeric. Specific terms are used as follows:

Qualitative data or Nonnumeric data:

When the data are classified according to some qualitative terms like honesty, beauty, employment, intelligence, occupation, sex, literacy, etc, the classification is termed as qualitative or descriptive or with respect to attributes.  Qualitative data are often termed as categorical data.

Univariate data:

The data identified on the basis of single characteristic is called as single attribute/univariate data. The number of students in a class room based on the characteristic gender. Here, we consider the characteristic as boys and girls.

Multi variate  data: 

The data identified on the basis of more than one characteristic is called as multivariate data. The number of students on a class room based on the characteristic height and gender. Here, we consider the characteristic boys and girls and also tall and short. If the data is classified into two or more classes with respect to a given attribute, it is said to be a manifold classification. For example, for the attribute intelligence the various classes may be, genius, very intelligent, average intelligent, below average and dull.

Quantitative data or numeric data:

Quantitative data are collected as countable numbers. They are usually subjected to statistical procedures such as calculating the mean, frequency distribution, standard deviation etc. Moving on to higher statistical analysis such as t-test, factor analysis, Analysis of variance, regression can also be conducted on the data. 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 can be ranked/ordered. It is possible to count and sorted in a particular order but data cannot be measured. Example: T-shirt size (large, medium, small).

Interval:

Measurements where the difference between values is measured by a fixed scale and has meaningful intervals but there is no true starting point(zero). Data collected is continuous which has a logical order with standard difference between values. Example: Temperature, Money, Education (In years)

Ratio:

Ratio variables are numbers with some base value and there is starting point(zero). Ratio responses will have order and spacing where multiplication makes sense too. Example: Height, weight.

Once collected data is classified on levels of measurement, then appropriate statistical methods can be used.

Depending on the variable types which is actually data can also be included in the categorization as shown