DATA TYPES / MASUREMENT
Variable can take on many different forms and levels sophistication.
The relationship between what
is being measured and the number that represent what is being measured known as
the levels measurement. Broadly speaking, variable can be categorical or
continuous, and can have different
levels of measurement.
I. Categorical variable:
I. Categorical variable:
A categorical variable
is made up categories.
The entities are different categories this known as
categorical variables which includes,
- Ø Binary variable
- Ø Nominal variable
- Ø Ordinal variable
Ø Binary : A categorical variable is one that name distinct
entities. In its simplest form it name just two distinct types things, and this
is known as Binary variables.
eg: male or female, alive or dead, pregnant or not, and
responding “Yes” or” No” to a question.
Ø Nominal : When two
things that are equivalent in some sense are given the same name (or
number),but there are more than two possibilities ,the variable is said to be a
nominal variables.
It should be obvious that if the variable is made up
of names , it is pointless to do arithmetic on them(if you multiply a human by
a cat, you do not get a hat ). However, sometimes
numbers are used to denote categories.
Eg: The numbers worn by players in a football team.
In football, the numbers of shifts denote specific field positions, so the
number 10 is always worn by the fly-half and the number 1 is always the hooker.
These numbers do not tell us anything other than what position the player
plays. We could equally have shirts with FH and H instead of 10 and 1.A number
10 players is not necessarily better than a number 1 .It is equally as daft to
try to do arithmetic with nominal scales.
Ø Ordinal : When
categories are ordered, the variables is known as ordinal variable. However, these
data tell us nothing about the differences between values. Ordinal scale, numbers
reflect their rank order or merits position within their own group or class
with respect to some quality, property or performance.
The
ordinal scale places events in order .The defect in such scale lice
in the fact that the units along the scale are unequal in size.
The difference in the
achievement scores between the first and the second merit position holder is
not necessarily equal to the difference between the second and third.
Eg: The beauty contest
winners are three. The names of the winners don’t provide any information about where they
came in the contest; however labeling them according to their performance
does-first, second and third. These categories are ordered. In using ordered
categories we now know that the woman who won was better than the women who
came second and third. Ordinal data, therefore, tell us more than normal data
but they still do not tell us about the difference between points on a scale.
II.
Continuous
Variables :
A
continuous variable is one that gives us a score for each person and can take
on any value on the measurement scale that we are using. Continuous variables
which includes,
Ø Interval variable
Ø Ratio variable
Ø Interval variable : Interval data are considerably the statistical tests
in this book rely on having data measured at this level. To say that data are
interval, we must be certain that equal intervals on the scale represent equal
differences in the property being measured.
Interval scales can have an arbitrary zero but it is not possible to
determine for them what may be called on absolute zero or the unique origin.
The primary limitation of the interval scale is the lack of a true zero. It
does not have the capacity to measure the complete absence of a trait or
characteristic.
Eg: On www.ratemyprofessors.com students are encouraged to rate their lecturers on
several dimension (some of the lectures’ rebuttals of their negative
evaluations are worth a look). Each dimension(i.e helpfulness,clarity,etc.)is
evaluated using a 5-point scale. For this scale to be interval it must be the
case that the difference between helpfulness ratings of 1 & 2 is the same
as the difference between say 3 & 4, or 4 & 5. Similarly, the
difference in helpfulness between ratings of 1 & 3 should be identical to the difference between ratings
of 3 & 5. Variables like this that
look interval (and are treated as interval) are often ordinary.
Ø Ratio variable : It constitutes the find and highest type of scale in
terms of measurement. Ratio scales have a absolute or true zero of measurement.
So the Ratios value of values along the scale should be meaningful. Here
measures are not only expressed in equal units but are also taken from a true
zero. The zero on such scales essentially means an absence of quality or
attributes being assessed.
Eg; All physical measurement are example of ratio
scale, such as length, width, weight, capacity etc… temperature
In the
measurement of all these attributes all the concerned measuring scales start
from a true zero. These scales easily
permit statements regarding the comparative ratio in relation to some quality
or property existing among the different individual or objects.
CONCLUSION
These proceding from the nominal scale
(the least precise type of scale) to ratio scale (the most precise) relevant
information is obtained increasingly. If the nature of the variables permits,
the researcher should use the scale that provides the most precise description. Researchers in physical science have the
advantage to describe variables in ratio scale form but the behavioral science
are generally limited to describe variable in interval scale form a less
precise type of measurement.
REFERENCES
REFERENCES
v DISCOVERING STATISTICS USING SPSS, ANDY FIELD,III EDITION.
v KOTTARI C.R, RESEARCH METHADOLOGY METHODS AND TECHNIQUES,
II EDITION, NEW AGE INTERNATIONAL PUBLISHERS.
v MANGAL S.K, STATISTICS IN PSYCHOLOGY AND EDUCATION,
II EDITION, ASOKE K GHOSH.
