2. OUTLINE
• Measurement as Tool of Research
– Introduction
– Measurement Scales
• Nominal Scale
• Ordinal Scale
• Interval Scale and Problems with Interval Scale
• Ratio Scale
– Validity and Reliability of Measurement
3. Introduction
• Measurement is limiting the data of any Phenomenon -
Substantial or Insubstantial, so that those data may be interpreted
and, ultimately compared to an acceptable qualitative or
quantitative standard.
• When we measure something, we set a limit that restrain the
data.
Example : 12 inches restraint a foot
• Substantial : Observable objects are measured.
Example : an engineer measuring the span of a bridge
• Insubstantial : These are things that exist only as concepts, ideas,
opinions, feelings, or other intangible entities.
Example : measuring the economic “health” of a business.
Example : Alzheimer- Mini Mental State Exam (MMSE)
Measurement as Tool of Research
4. Introduction Conti…
• Data have been transformed into units of discovery, of
revelation, of enlightenment, of insight that hasn’t seen before.
• In research, standards are like, norms, averages, conformity to
expected statistical distributions, accuracy of description.
• Measurement is ultimately a comparison.
• Therefore, measurement is indeed a tool by which data may be
inspected, analyzed, and interpreted.
5. Nominal Scale of Measurement
• A Nominal Scale is a measurement scale, in which numbers
serve as “tags” or “labels” only, to identify or classify an
object.
• A nominal scale measurement normally deals only with non-
numeric (Quantitative) variables or where numbers have no
value.
• Assign a specific name to anything and restrict that thing to
meaning of it.
• Assign names to data in order to measure it.
Example : Girls and Boys.
• Things can divided in infinite number of ways.
Example : home site, town,
Example : Karnataka, Goa, Maharashtra, UP
Measurement Scales
6. Ordinal Scale of Measurement
• “Ordinal” indicates “order”,
• So, we may think in terms of “ < ” or “ >” .
• Ordinal data is quantitative data which have naturally
occurring orders and the difference between is unknown.
Example : 1st , 2nd, 5th rank.
• It can be named, grouped and also ranked.
• One object is bigger or better or more of anything than
another.
Example : we can measure members of workforce by
grades of proficiency : unskilled, semiskilled or skilled.
Example : level of education : unschooled, high school,
college, graduate
7. Interval Scale of Measurement
• The interval scale is defined as a quantitative measurement
scale where the difference between 2 variables is meaningful.
• Interval scales are numeric scales in which we know only the
order, but also the exact differences between the values.
• Example : Celsius temperature – difference between 90 and 70
degrees is 20 degrees, as 40 and 60.
• Time-difference between 6PM and 7PM is 1 hour as 4PM and
5PM.
• Example : Rate the battery life of inverter.
2
1 3 4 5
8. Problem with Interval Scale
• Here we don’t have “true zero” but may have arbitrary zeros.
• There is no such thing as “ No Temperature”.
• Without a true zero, it is impossible to compute ratios.
• Interval data Add and Subtract
• We can’t Multiply or Divide
• Example : 20 0C + 10 0C = 30 0C
40 0C is not twice as hot as 20 0C.
9. Ratio Scale of Measurement
• It is a type of variable measurement scale which
is quantitative in nature.
• Ratio scale allows any researcher to compare the intervals or
differences.
• Tell us exact value between units.
• Possesses a zero point or character of origin.
• And also have an absolute zero.
• These values can be meaningfully added, subtracted,
multiplied and divided.
• Example : Monthly income of surgeons,
• Example : Height and Weight.
10. • We can summarize our description of the four scales in this
way:
– One object is different from another, we have a nominal
scale;
– One object is bigger or better or more of anything than
another, we have an ordinal scale;
– One object is so many units (degrees, inches) more than
another, we have an interval scale;
– One object is so many times as big or bright or tall or heavy
as another, we have a ratio scale.
11.
12. Reliability and Validity of Measurement
Reliability
• Reliability refers to how consistently a method measures
something.
• If the same result can be consistently achieved by using the
same methods under the same circumstances, the measurement
is considered reliable.
• Reliability is about the consistency of a measure.
• Example : if we measure the temperature of a liquid sample
several times under identical conditions. The thermometer
displays the same temperature every time, so the results are
reliable.
13. Validity
• Validity refers to how accurately a method measures what it is
intended to measure.
• If research has high validity, that means it produces results that
correspond to real properties, characteristics, and variations in
the physical or social world.
• High reliability is one indicator that a measurement is valid. If
a method is not reliable, it probably isn’t valid.
• Validity is about the accuracy of a measure.
14. What does
it tell you?
The extent to which the results
can be reproduced when the
research is repeated under the
same conditions.
The extent to which the
results really measure what
they are supposed to
measure.
How is it
assessed?
By checking the consistency of
results across time, across
different observers, and across
parts of the test itself.
By checking how well the
results correspond to
established theories and
other measures of the same
concept.
How do
they relate?
A reliable measurement is not
always valid: the results might
be reproducible, but they’re not
necessarily correct.
A valid measurement is
generally reliable: if a test
produces accurate results,
they should be reproducible.
Reliability Vs Validity
Reliability Validity
