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easy data analysis.

Ordinal scale
The ordinal scale works on the concept of the relative position
of objects or labels according to the choice or preference of the
individual. It ‘orders’ the members of a population in ranks, from
highest to lowest or lowest to highest. Although each value is
ranked, there is no information that specifies what differentiates

the categories from each other. Range values do not possess the
arithmetic property of equal intervals or true zero points. This
◆ Description and means that, with ordinal data, we only know that one member of a
order
population has more or less quality than another member, but we
do not know how much more or less. Ordinal scaling maintains
descriptive qualities along with an intrinsic order, but it does not
have a scaling origin and therefore the distance between variables
cannot be calculated. The descriptive qualities indicate labelling
properties similar to the nominal scale, in addition to which, the
ordinal scale also has a relative position of variables.

In nutshell ordinal scale has the following properties:

◆ A rating scale in which numbers are assigned to objects to
indicate how well objects possess certain characteristics.
◆ Can determine if an object has more or less of a characteristic
than another object, but not how much more or less (1 ,
st
nd
◆ No distance and 2 ….).
origin
◆ Any series of numbers can be assigned preserving ordered
relationships between objects.

◆ In addition to the counting operation allowed for nominal
scale data, ordinal scales allow the use of statistics based on
percentiles, for example, percentile, quartile, median (such as
top ten percentiles or the 1 decile).
st
For example: A software company may need to ask its users:

How would you rate our application?

◆ Excellent
◆ Very Good

◆ Good
◆ Bad
◆ Very bad

Interval scale

Interval scales incorporate all the properties of nominal and
ordinal scales and also indicate the distance or interval between

102 SGOU - SLM -MCom Research Methodology