LEVELS OF MEASUREMENT
Data can be classified according to four levels of measurement:
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Nominal
Ordinal
Interval
Ratio
The level of measurement determines the nature of the statistical manipulation allowed.
Nominal measurement
Here the categories of variables differ
in name only, not in value. They cannot
be arranged in an ordering scheme.
Ordinal measurement
There is an ordered relationship among
the categories.
In other words, a category assigned a 1
can be considered higher than a
category assigned a 2, which would be
higher than category 3.
[While there is a rank order in the
numbers assigned to the categories of
the variable, the distance between the
categories is not equal or not known.]
Interval measurement
Used when the distance (or interval)
between any two categories of
variables is known. This is the first
truly quantitative level of measurement.
The numbers assigned to categories of
interval variables have all the
characteristics of nominal and ordinal
variables plus the added characteristic
of a constant unit of measurement
between categories that are equally
spaced.
However, there is no natural zero
starting point.
Ratio measurement
Is an interval measure that includes a
natural starting point
UNICEF M&E Training Resource
Examples
Statistical manipulations
For example, the variable “sex” has
two categories: male and female.
The researcher can assign a 1 to the
category “male” and a 2 to the
category “female”, or vice versa. The
only meaning these numbers have is
to distinguish one category from the
other.
For example, mothers are asked
about their attitude toward
breastfeeding. The response
categories might be assigned
numbers in the following manner:
1 = Approve very much
2 = Approve somewhat
3 = Approve very little
4 = Do not approve
The numbers assigned to the
categories not only distinguish
whether things are in the same
category or a different category (as
they do with nominal variables), but
they also indicate an ordered ranking
from 1 which, in this case, equals
high approval, to 4, which equals low
approval.
Years and temperature are interval
variables because the distance
between the categories is known and
constant but there is no natural zero
point (i.e. the year 0 has been
arbitrarily set as the temperature 0
expressed in centigrades). A person
aged 34 is four years older than a
person aged 30. A child, for
example, that is placed in category 5
is not only different (nominal) from
one placed in category 6 but also
younger (ordinal), and one year
younger (interval).
Nominal measurement is the weakest
of all measurements because it allows
only limited statistical manipulations.
One can calculate the mode (the most
frequently occurring number) and a
percentage distribution, but one
cannot calculate a mean. It makes no
sense to speak of the "mean sex."
With ordinal variables, one can use
statistical manipulations, like mode,
frequency distribution, median,
percentile, and various nonparametric statistical tests. One
cannot use mean, standard deviation,
or a Pearson product-moment
correlation because the distance or
interval between the categories is not
known. For example, in the example
provided to the left, one does not
know if the distance between 1
(approve very much) and 2 (approve
somewhat) is the same as the
distance between 3 (approve very
little) and 4 (do not approve).
Weights, time lengths, distances are
all ratio variables because the
distance between the categories is
known and constant and the ratio is
meaningful.
With interval variables, one can use
such measures as mean, standard
deviation, the Pearson productmoment correlation, and many other
parametric statistics.
Module 6.1.1
With interval variables, one can use
many parametric statistics.
However it is important to remember
that ratios are meaningless (e.g. 90°
is not twice as hot as 45°).
Levels of measurement - Page 1/1