This document discusses different types of data: - Nominal categorical data has no units or meaningful ordering between categories like sex or blood type. - Ordinal categorical data can be ordered but not measured, like the Glasgow Coma Scale. - Discrete metric data comes from counting things with units like number of hospital visits. - Continuous metric data results from measurement and has real number values and units like weight or blood pressure. The type of analysis depends on whether the data is categorical or metric, nominal or ordered, discrete or continuous.

1. Statistics
“One”
Mohamed Ahmed Hefny, MD.

3. • Before you can successfully analyze a data set, you should
understand the differences among different types of data or
variables.
• The type of analysis or test that you will run depends largely on the
type of data you have.
• These terms may be a bit confusing because of overlapping or
similar definitions.

4. Data
Quantitative
(Metric)
Qualitative
(Categorical)
Continuous Discrete OrdinalNominal

6. Categorical data

8. Nominal categorical data
• These data are nominal categorical data (or just nominal
data for short).
• The data are ‘nominal’ because it usually relates to named
things, such as sex, occupation, blood type, or ethnicity. It is
particularly not numeric.
• It is ‘categorical’ because we allocate each value to a
specific category. i.e., we allocate each Male & Female in a
class

9. Notice two things about this data, which is typical of all nominal
data:
• The data do not have any units of measurement.
• The ordering of the categories is arbitrary. In other words, the
categories cannot be ordered in any meaningful way.
We could just as easily have written the number of males and
females in any order suitable.

10. Ordinal categorical data
• It is ordinal because the values can be meaningfully ordered, and
it is categorical because each value is assigned to a specific
category. i.e. Glasgow Coma Scale
• Notice two things about this variable, which is typical of all
ordinal variables
1. The data do not have any units of measurement (so the same as
that for nominal variables).
2. The ordering of the categories is not arbitrary, as it is with
nominal variables.

11. 1. Because ordinal data are not real numbers, it is not appropriate
to apply any of the rules of basic arithmetic to this sort of data.
You should not add, subtract, multiply or divide ordinal values.
2. This limitation has marked implications for the sorts of analyses
that we can do with such data.
3. It should be noted that ordinal data are almost always integer,
that is, they have whole number values.

12. Metric data

14. Discrete metric data
• Discrete metric data comes from counting. Counting is a form of
measurement – hence the name ‘metric’.
• The data is ‘discrete’ because the values are in discrete
(separate) steps; for example, 0, 1, 2, 3 and so on.
• Other examples of discrete metric data would include number
of deaths, number of pressure sores, number of angina attacks,
number of hospital visits and so on.

15. • Metric discrete variables can be counted and can have units of
measurement – ‘numbers of things’
• They produce data which are real numbers and are always
integers (i.e. whole numbers).

16. Continuous metric data
Metric continuous data result from measurement and they
have units of measurement.
• The data are real numbers.
• Because metric data values are real numbers, you can apply
all of the usual mathematical operations to them.
• This opens up a much wider range of analytic possibilities
than is possible with either nominal or ordinal data.
• i.e. weight and blood pressure

17. How can I tell what type of variable I’m
dealing with?

18. Has the data got units ?
(Includes number of things)
Can the data be put
in meaningful order?
Do the data come from
counting or measuring ?
Categorical
nominal
Categorical
ordinal
Continuous
metric
Discrete
metric
No Yes
No Yes
Counting Measuring

20. Thank You