TYPES OF MEASUREMENT OF DATA
There are four different types of measurement scales (nominal, ordinal, interval and ratio). Each
of the four scales, respectively, typically provides more information about the variables being
measured than those preceding it. That is the reason why the terms "nominal", "ordinal",
"interval", and "ratio" are often referred to as levels of measure. Now let's look at the differences
so that you can tell them apart.
Nominal Variables
Where does the word "nominal" comes from? It has to do with naming. So nominal comes from
name and that is all you can do with variables measured on nominal scales (nominal variables).
The important thing is there is no measure of distance between the values. You're either
married or not married. The answer is determined, yes or no. So there is no question of how far
apart in a quantitative sense those categories are. They are just names. Nominal scales name
and that is all that they do. Some other examples are sex (male, female), race (black, hispanic,
oriental, white, other), political party (democrat, republican, other), blood type (A, B, AB, O), and
pregnancy status (pregnant, not pregnant).
Ordinal Variables
In the next kind of variable you have a little more sophistication than you can get with just
names alone. What does ordinal imply? Ordinal implies order. And, order means ranking. So the
things being measured are in some order. You can have higher and lower amounts. Less than
and greater than are meaningful terms with ordinal variables where they were not with nominal
variables. For example, you don't rank male and female as higher and lower. But you do rank
stages of cancer, for example, as higher and lower. You can rank pains as higher or lower. So,
ordinal variables give you a more sophisticated level of measure - a finer tuned level of
measurement. But you have now added only this one element having to do with ranking. You
know that something is higher than something else, or lower than something, or more painful
than something, or less painful than something.
So, ordinal scales both name and order. Some other examples of ordinal scales are rankings
(e.g., football top 20 teams, pop music top 40 songs), order of finish in a race (first, second,
third, etc.), cancer stage (stage I, stage II, stage III), and hypertension categories (mild,
moderate, severe).
Interval Variables
What about interval variables? How are they different? Why are Celsius and Fahrenheit
temperature variables called interval variables? They are called interval variables because the
intervals between the numbers represent something real. This is not the case with ordinal
variables.
Interval variables have the property that differences in the numbers represent real differences in
the variable. Another way to say this is that equal differences in the numbers on the scale
represent equal differences in the underlying variables being measured. For example, look at
the difference between 36 degrees and 37 degrees compared to the difference between 40
degrees and 41 degrees on either Fahrenheit or Celsius temperatures? Is the difference the
same? Because the differences in the numbers are the same, when you have an interval
variable you know temperature intervals are the same.
So, with interval variables you now know not only whether one value is higher than another, but
that the distances between the intervals on the scales are the same. Again, you have a higher
level of information. Interval scales not only name and order, but also have the property that
equal intervals in the numbers measured represent real equal differences in the variables.
Examples of interval scales include the Fahrenheit and Celsius temperatures previously
mentioned, Exam and IQ scores. In general, many of the standardized tests of the
psychological, sociological and educational displines use interval scales. Interval measures all
share the property that the value of zero is arbitrary. On the Celsius scale, for example, 0 is the
freezing point of water. On the Fahrenheit scale, 0 is 32 degrees below the freezing point of
water.
Ratio Variables
Ratio variables have all the properties of interval variables plus a real absolute zero. That is,
value of zero represents the total absence of the variable being measured. Some examples of
ratio variables are length measures in the english or metric systems, time measures in seconds,
minutes, hours, etc., blood pressure measured in millmeters of mercury, age, and common
measures of mass, weight, and volume.
They are called ratio variables because ratios are meaningful with this type of variable. It makes
sense to say 100 feet is twice as long as 50 feet, because length measured in feet is a ratio
scale. Likewise it makes sense to say a Kelvin temperature of 100 is twice as hot as a Kelvin
temperture of 50 because it represents twice as much thermal energy (unlike Fahrenheit
temperatures of 100 and 50). With ratio variables, the only difference from interval variables is
that you have a true zero so that you can actually talk about ratios. That is a person's lung
capacity can be twice somebody else's lung capacity. In order to make those kinds of
statements you have to have be able to compute meaningful ratios and you can only do that if
you have a true zero. But really for the purposes of any statistical tests it makes no difference
whether you have interval or ratio variables.
Reference : http://www.uth.tmc.edu/uth_orgs/educ_dev/oser/L1_2.HTM#FIG1_1