There
are four levels of variables. These levels are listed below in order of their
precision. It is essential to be able to
identify the levels of data used in a research design. They are directly associated with
determining which statistical methods are most appropriate for testing research
hypotheses.
Nominal: Classifies objects by type or characteristic (sex,
race, models of vehicles, political jurisdictions)
Properties:
1.
categories
are mutually exclusive (an object or characteristic can only be contained in one category
of a variable)
2.
no
logical order
Ordinal: classifies objects by type or kind but also has
some logical order (military rank, letter grades)
Properties:
1.
categories
are mutually exclusive
2.
logical
order exists
3.
scaled
according to amount of a particular characteristic they possess
Interval: classified by type, logical order, but also
requires that differences between levels of a category are equal (temperature in degrees
Celsius, distance in kilometers, age in years)
Properties:
1.
categories
are mutually exclusive
2.
logical
order exists
3.
scaled
according to amount of a particular characteristic they possess
4.
differences
between each level are equal
5.
no
zero starting point
Ratio: same as interval but has a true zero starting point
(income, education, exam score). Identical to
an intervallevel scale except ratio level data begin with the option of total absence of
the characteristic. For most purposes, we
assume interval/ratio are the same.
