There are four types of data used in statistics: nominal data, ordinal data, discrete data, and continuous data.
Nominal and ordinal data fall under the umbrella of categorical data, while discrete data and continuous data fall
under the umbrella of continuous data.
Qualitative Data
Categorical or qualitative data labels data into categories.
Categorical data is defined in terms of natural language
specifications. For example, name, sex, country of origin, are
categories that represent qualitative data. There are two
subcategories of qualitative data, nominal data and ordinal data.
Nominal Data
Nominal data is the simplest data type. It classifies data purely by
labeling or naming values. The labeled categories have no order,
and are mutually exclusive (no overlap). Nominal data cannot be
ordered and measured. For example, sex, home town, country of
origin, favorite candy bar, etc. cannot be ordered. There is no
category that has greater value than another category. Please note
that there are nominal data represented by numbers. These
numbers may make you think the data has order, but the fact is that
the order does not exist, for example, zip codes. Nominal data are
examined using a method that groups the data into categories, and
then the frequency or the percentage of the data in each category
can be calculated. Nominal data is visually represented using a pie
chart.
Ordinal Data
When the categories have a natural order, the categories are said to
be ordinal. It can be ordered and measured. For example education
level (H.S. diploma; 1 year certificate; 2 year degree; 4 year degree;
masters degree; doctorate degree), satisfaction rating (extremely
dislike; dislike; neutral; like; extremely like), etc. are categories that
have a natural order to them. Ordinal data are commonly used for
collecting demographic information (age, sex, race, etc.). This is
particularly prevalent in marketing and insurance sectors, but it is
also used by governments (e.g. the census), and is commonly used
when conducting customer satisfaction surveys. Ordinal data is
commonly represented using a bar graph.
Quantitative Data
Numerical or quantitative data involves numbers and there is always order
to those numbers. Quantitative data gives information about the measure
of a specific thing.
The distances of adjacent values (e.g., marks on a ruler) should be equal.
For example, the distances between marks on a weight scale are equal.
However, data points from a typical 5-point rating scale (e.g., 1-5) are not
quantitative data, even though they are numbers. This is because the
lengths (or strengths) between the adjacent points are NOT the same.
Quantitative data has two subcategories, discrete data and continuous data.
Discrete Data
The data is discrete when the numbers do not touch each other on a real
number line (e.g., 0, 1, 2, 3, 4…). Discrete data is whole numerical values
typically shown as counts and contains only a finite number of possible
values. For example, the number of visits to the doctor, the number of
students in a class, etc. Discrete data is typically represented by a
histogram.
Continuous Data
The data is continuous when it has an infinite number of possible values
that can be selected within certain limits. (i.e., the numbers run into each
other on a real number line). Continuous data is data that can be
calculated. It has an infinite number of possible values that can be selected
within certain limits. Examples of continuous data are temperature, time,
height, etc. Continuous data is typically represented by a line graph.
Figure 1. Types of data.
