There are four data types (or scales of measurement), namely
nominal, ordinal, interval and ratio.
(1)
Norminal-scaled data - the nominal level of measurement classifies data
into mutually exclusive (non-overlapping), exhausting categories in
which no order or ranking can be imposed on the data.
F
Examples are the labels sex, programme of study, species name, marital
status, religious affiliation, employment status, education-level .
F
These labels are only used to identify an attribute of the variable.
F
Under nominal measurements, ’numbers’ are simply to identify, classify,
categorize or distinguish.
F
For instance, scores 0 for male and 1 for female are simply used to
identify, distinguish, categorize or classify the subject by sex.
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Introduction to Statistics and Biometry
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Types of Data
The next level of measurement is called the ordinal level.
(2)
Ordinal-scaled data - ordinal level of measurement classifies data into
categories that can be ranked; however, precise differences between the
ranks do not exist.
F
Ordinal data are generated from ranked responses (also generated from
a counting process).
F
Examples include: grade (A,B,C,D), judging(first, second, third place),
rating scale(excellent, good, poor), ranking of players etc.
F
The observation for each variable possesses characteristics of nominal
data in that each response rating is a label for excellent, good or poor
quality.
F
In addition, the data can be ranked with respect to quality.
F
An ordinal variate may also be represented by scores such as 1 = poor,
2 = fair, 3 = good, etc.
F
Thus, ordinal scaled-data have the properties of nominal data but the
order or rank of the data is meaningful or important and data can be
numeric or nonnumeric
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Introduction to Statistics and Biometry
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Types of Data
The third level of measurement is called the interval level.
(3)
Interval-scaled data - interval level of measurement ranks data, and
precise differences between units of measure do exist; however, there is
no meaningful zero. These are really numeric.
F
This level differs from the ordinal level in that precise differences do
exist between units.
F
Temperature is one example of interval measurement, since there is a
meaningful difference of 1
◦
C
between each unit, such as 35 and 36
◦
C
.
F
IQ is another example of such a variable. There is a meaningful
difference of 1 point between an IQ of 109 and an IQ of 110.
F
One property is lacking in the interval scale -there is no true zero.
F
For example, IQ tests do not measure people who have no intelligence.
For temperature, 0
◦
does not mean no heat at all.
Lecture Number 1
Introduction to Statistics and Biometry
September 19, 2016
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Types of Data
The final and highest level of measurement is called the ratio level.
(4)
Ratio-scaled data - ratio level of measurement possesses all the
characteristics of interval measurement, and there exists a true zero. In
addition, true ratios exist when the same variable is measured on two
different members of the population.
F
Examples of ratio scales are those used to measure height, weight,
area, age, time, money etc.

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