• Data categories should
be mutually exclusive and exhaustive.
•Mutually exclusive: each possible survey
response should belong to only one category, whereas
•Exhaustive: categories should
cover the entire set of possibilities.
Data Categories
•Nominal
data: different categories of
data, but they lack an order. (A binary variable is a nominal variable with
only two categories.)
Examples: sex (binary),
name, nationality, eye color, city
•Ordinal
data: Categorical data that
has an intrinsic order. It only shows sequence. The distance between categories
is not really standart, so we cannot add, subtract, etc.
Examples: Likert items
(level of agreement), restaurant/hotel rankings, race rankings (first and
second in a competition).
•Discrete
data: only has integers and
decimals are meaningless. We usually collact discrete data by counting
items/events.
Example: The number
of children in a family. The number of votes for a party.
•Continuous
data: can take any range of
values.
Examples: weight,
height, age, heat.
•Interval
data: Each position is equally
distant from one another, but ratios are meaningless. Zero is not meaningful
(it is arbitrary)
Example: Heat. 20 degree
and 25 degrees are 5 degrees different. 35 degrees and 40 degrees are also 5
degrees different. But 40 degrees is NOT twice as hot as 20.
•Ratio
Data: Numbers can be multiples
of each other. can be multiplied and divided. Zero has a meaning.
•Example: Age, weight,
height.
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