Four Types of Scales
Nominal Scale
A nominal scale uses numerals to identify objects, individuals, events, or groups. The scale is
simply used to (nominally) classify people, objects or events into different groups. It is not
intended to say whether one group is superior to or greater than the other.
Data from this scale can be analyzed by computing frequency counts, percentages/modes.
Some examples of nominal scale are:
Gender
Geographic Area
(1) Male (2) Female
(1) Urban (2) Rural (3) Suburban
Ordinal Scale
An ordinal scale is richer than a nominal scale. In addition to identification, possible with a
nominal scale, the numerals provide information about the relative amount of some characteristic.
It allows you to determine whether one point on the scale is greater than another point.
Data from this type of scale can be analyzed by computing frequency counts, percentages/modes
as in the nominal scale. In addition, you can compute the median.
An example of ordinal scale:
Please rank the following fax machines from 1 to 5 with 1 being the most preferred and 5
the least preferred.
_____ Panasonic
_____ Toshiba
_____ Sharp
_____ Savin
_____ Ricoh
Interval Scale
An interval scale possesses all the properties of nominal and ordinal scales plus the intervals
between consecutive points on the scale are equal. In addition to computing frequencies,
percentages, median and mode, data from interval scales permit you to take averages or means.
Averages are commonly used as summary statistics in many research activities (e.g., the average
temperature in Bahamas in December is 72 degrees Fahrenheit). Because of this attractive
feature, interval scales are commonly used for measuring marketing concepts/constructs.
The temperature scale in a thermometer is an example of an interval scale. In marketing, the
following question would be deemed an interval scale:
How important is the following attribute when purchasing a dress shoe? (Please circle
one number)
Shoe fit
Not at all
Important
Somewhat
Unimportant
Neutral
Somewhat
Important
Very
Important
1
2
3
4
5
Technically, the scale is ordinal. That is, if I say shoe fit is somewhat important (4) and you say it
is very important (5), then your importance rating is greater than mine (semblance of ordering).
However, by using appropriate phrases and by equidistant positioning, the scale points 1-5, for all
intents and purposes, we take it as an interval scale. That is, if I circle 3, you circle 5, and your
friend circles 4, we can say the difference in importance rating between your friend and myself
(4-3) is the same as the difference in importance rating between yourself and your friend (5-4).
We can also say the average importance rating of all three of us is [(3+5+4)/3]
Ratio Scale
Ratio scale is the richest of all scales. It incorporates all the properties of nominal, ordinal, and
interval scales, plus it includes a meaningful absolute zero point that allows you to take ratio of
numbers. Data from this scale can be analyzed by computing frequencies, percentages, mean,
median and mode, as well as by computing higher-order statistics such as geometric mean (which
we do not need to worry about!)
Examples of ratio scale are:
What is your age in years? _____
What is your weight in pounds? _____
How many gallons of milk do you drink per week? _____
Identifying the Type of Scale
Because the type of scale dictates the data analysis method, it is important to correctly identify
the scale used in a question. Use the following guideline. Assume person A checks one point on
the scale, B checks the next point, and C checks the point next to what B has checked. What can
you say about A, B and C?




If you can only say that A, B and C are different – it is a nominal scale.
If you can also say that A is greater or less than B in some respects captured in the scale and
likewise for A and C and B and C – then it is an ordinal scale.
If you cans say the difference between A and B is the same as the difference between B and C
(intervals are equal), then it is an interval scale.
If there is a meaningful zero point, and if it is meaningful to say A is so many times B, then it
is a ratio scale.
Here are some examples to make sure we can identify the scale correctly.
Please indicate your age by circling a number between 1 and 4 in the following question:
Age (Years):
(1) 0-12 years (2) 13-25 years (3) 26-40 years (4) over 40 years
What type of scale is this?
Suppose A checks (1), B checks (2) and C checks (3).
Can we say A, B and C are different? Yes – hence it is at least nominal.
Can we say A is younger than B who is younger than C? Yes – hence it is at least ordinal.
Can we say difference between ages of A and B is the same as the difference in ages of B and C?
NO. It is possible A is 8 years old, B is 13 years and C is 40 years. Hence, it is not interval.
So, the above scale is ordinal.
Consider the same age question asked in a different way.
What is your age in years? ___
Though the question does not look like a scale, it is implicitly one in the form of 0 1 2 3 ..27 28
29.…etc.
Suppose A says 4, B says 8 and C says 12 years old. What can we say from this scale about the
ages of A, B and C?
Can we say A, B and C are different? Yes – hence it is at least nominal.
Can we say A is younger than B who is younger than C? Yes – hence it is at least ordinal.
Can we say difference between ages of A and B is the same as the difference in ages of B and C?
Yes, hence it is interval.
Is there a meaningful zero point? Yes, technically you are 0 years old at the time of birth. Hence, I
can say B is twice as old as A and C is three times as old as A. Hence, it is a ratio scale.
So, the above scale is ratio.
Note that it is not what you measure but how you measure that determines the type of scale. In the
above illustration, age was measured as an ordinal scale in the first example and as a ratio scale in
the next example. From a technical standpoint, it is better to use the richest ratio scale, where
possible. But you should also consider other factors. For example, I generally use age questions
as an ordinal scale (as in the first example above) because respondents may be uncomfortable
stating their exact age in the ratio-scaled question.
Importance of Scales
Understanding these four types of scales is very important, especially when you do survey data
analysis. I have seen many researchers, consultants and professors incorrectly identify the type of
scale and/or do the wrong kind of data analysis for a given scale. Here are two common errors.
1. In research involving rank ordering various objects, the rank order is an ordinal scale. So you
cannot take the mean rank, a common erroneous procedure adopted by many researchers and
reporters. For example, if you ask respondents to rank 10 car companies from 1 to 10, you
cannot take the average and say the mean rank for Ford is 3.2.
2. In a similar vein, many weather reporters observe the temperature in May to be (say) 80
degrees Fahrenheit, and in November to be 40 degrees and claim May is twice as hot as
November. This is incorrect because the temperature scale is only an interval scale. The zero
in the scale is arbitrary (not universal/meaningful) – i.e., 0 degrees Fahrenheit is – 32 degrees
Centigrade. You cannot take a ratio of two temperatures.