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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 interval-level 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.
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