Business Research Method - Unit III, AKTU, Lucknow Syllabus, Research Methodology - Topics Covered - Scaling & Measurement techniques: Concept of Measurement: Need of Measurement; Problems in measurement in management research – Validity and Reliability. Levels of measurement – Nominal, Ordinal, Interval, Ratio. Attitude Scaling Techniques: Concept of Scale – Rating Scales viz. Likert Scales, Semantic Differential Scales, Constant Sum Scales, Graphic Rating Scales – Ranking Scales – Paired comparison & Forced Ranking – Concept and Application.

1. Unit III

2. Unit III - Scaling & measurement
techniques
• Concept of Measurement: Need of
Measurement; Problems in measurement in
management research – Validity and Reliability.
Levels of measurement – Nominal, Ordinal,
Interval, Ratio.
• Attitude Scaling Techniques: Concept of Scale –
Rating Scales viz. Likert Scales, Semantic
Differential Scales, Constant Sum Scales, Graphic
Rating Scales – Ranking Scales – Paired
comparison & Forced Ranking – Concept and
Application.

3. Concept of Measurement
• Definition Metrology is the name given to the
science of pure measurement. Engineering
Metrology is restricted to measurements of
length & angle. Measurement is defined as the
process of numerical evaluation of a dimension
or the process of comparison with standard
measuring instruments
• Quantifying the dependent variable
•

4. Importance or Need of measurement
• Research conclusions are only as good as the data on which they
are based
• Observations must be quantifiable in order to subject them to
statistical analysis
• The dependent variable(s) must be measured in any quantitative
study.
• The more precise, sensitive the method of measurement, the
better.
• Establish Standard Interchangeability.
• To check Customer Satisfaction
• To Validate the design
• Physical parameter into meaningful number.
• True dimension Evaluate the Performance.

5. Direct measures
• Physiological measures
– heart rate, blood pressure, galvanic skin response,
eye movement, magnetic resonance imaging, etc.
• Behavioral measures
– in a naturalistic setting.
• example: videotaping leave-taking behavior
(how people say goodbye) at an airport.
– in a laboratory setting
• example: videotaping married couples’
interactions in a simulated environment

6. Indirect measures
• Relying on observers’ estimates or perceptions
– indirect questioning
• example: asking executives at advertising firms if they think their
competitors use subliminal messages.
• example: asking subordinates, rather than managers, what
managerial style they perceive their supervisors employ.
• Unobtrusive measures.
– measures of accretion, erosion, etc.
• example: studying discarded trash for clues about lifestyles, eating
habits, consumer purchases, etc.

7. Levels of data
• Nominal
• Ordinal
• Interval (Scale in SPSS)
• Ratio (Scale in SPSS)
nominal
ordinal
interval
ratio

8. Nominal data
• a more “crude” form of data:
limited possibilities for statistical
analysis
• categories, classifications, or
groupings
– “pigeon-holing” or labeling
• merely measures the presence
or absence of something
– gender: male or female
– immigration status;
documented,
undocumented
– zip codes, 90210, 92634,
91784.
• nominal categories aren’t
hierarchical, one category isn’t
“better” or “higher” than
another
• assignment of numbers to the
categories has no mathematical
meaning.
• nominal categories should be
mutually exclusive and
exhaustive.

9. Nominal data-continued
• nominal data is usually
represented “descriptively”
• graphic representations include
tables, bar graphs, pie charts.
• there are limited statistical tests
that can be performed on
nominal data
• if nominal data can be converted
to averages, advanced statistical
analysis is possible.

10. Ordinal data
• more sensitive than nominal
data, but still lacking in precision
• exists in a rank order, hierarchy,
or sequence
– highest to lowest, best to
worst, first to last
• allows for comparisons along
some dimension
– example: Mona is prettier
than Fifi, Rex is taller than
Niles
• examples:
– 1st, 2nd, 3rd places finishes in
a horse race
– top 10 movie box office
successes of 2006
– bestselling books (#1, #2, #3
bestseller, etc.)
2nd 3rd1st

11. More about ordinal data
• no assumption of “equidistance” of numbers
– increments or gradations aren’t necessarily uniform.
• researchers do sometimes treat ordinal data as if it were
interval data
• there are limited statistical tests available with ordinal
data.

12. Interval data (scale data)
• represents a more sensitive type of data or
sophisticated form of measurement.
• assumption of “equidistance” applies to data or
numbers gathered.
– gradations, increments, or units of measure are
uniform, constant.
• examples:
– Scale data: Likert scales,
– Stanford Binet I.Q. test
– most standardized scales or diagnostic instruments
yield numerical scores

13. More about interval data
• scores can be compared to one another, but in relative, rather
than absolute terms.
– example: If Fred is rated a “6” on attractiveness, and Barney a “3,” it
doesn’t mean Fred is twice as attractive as Barny
• no true zero point (a complete absence of the phenomenon
being measured)
– example: A person can’t have zero intelligence or zero self esteem.
• scale data is usually aggregated or converted to averages.
• amenable to advanced statistical analysis.

14. Ratio data
• the most sensitive, powerful type of data
– ratio measures contain the most precise
information about each observation that is
made
• examples:
– time as a unit of measure
– distance as a unit of measure (setting an
odometer to zero before beginning a trip)
– weight and height as units of measure

15. More about ratio data
• more prevalent in the natural sciences,
less common in social science research
• includes a true zero point (complete
absence of the phenomenon being
measured)
• allows for absolute comparisons
– If Fred can lift 200 lbs and Barney can lift 100
lbs, Fred can lift twice as much as Barney, e.g.,
a 2:1 ratio

17. VALIDITY AND RELIABILITY

18. • Sound measurement must meet the tests of
validity, reliability and practicality. In fact, these
are the three major considerations one should
use in evaluating a measurement tool. “Validity
refers to the extent to which a test measures
what we actually wish to measure. Reliability
has to do with the accuracy and precision of a
measurement procedure ... Practicality is
concerned with a wide range of factors of
economy, convenience, and interpretability ...”

19. Commonly used terms…
“She has a valid point”
“My car is unreliable”
…in science…
“The conclusion of the study was not valid”
“The findings of the study were not reliable”.

20. Some definitions…
• Validity
“The soundness or appropriateness of a
test or instrument in measuring what it is
designed to measure”
(Vincent 1999)

21. Some definitions…
• Validity
“Degree to which a test or instrument
measures what it purports to measure”
(Thomas & Nelson 1996)

22. Some definitions…
• Reliability
“…the degree to which a test or measure
produces the same scores when applied in
the same circumstances…”
(Nelson 1997)

23. Types of Experimental Validity
• Internal.
– Is the experimenter measuring the effect of the
independent variable on the dependent variable?
• External.
– Can the results be generalised to the wider
population?

24. Reliability
• The test of reliability is another important test of
sound measurement. A measuring instrument is
reliable if it provides consistent results.
• Reliable measuring instrument does contribute
to validity, but a reliable instrument need not be
a valid instrument.
• For instance, a scale that consistently
overweighs objects by five kgs., is a reliable
scale, but it does not give a valid measure of
weight. But the other way is not true i.e., a valid
instrument is always reliable.

25. Reliability
• Two aspects of reliability viz., stability and equivalence
deserve special mention.
• The stability aspect is concerned with securing consistent
results with repeated measurements of the same person
and with the same instrument. We usually determine the
degree of stability by comparing the results of repeated
measurements.
• The equivalence aspect considers how much error may get
introduced by different investigators or different samples
of the items being studied. A good way to test for the
equivalence of measurements by two investigators is to
compare their observations of the same events.

26. Attitude
• An attitude is viewed as an enduring
disposition to respond consistently in a given
manner to various aspect of the world,
including persons, events and objects.
– Attitude cannot be measured directly
– Attitude is derived from the perceptions
– Attitude are indirectly observed

27. Attitude
Attitude
Cognitive Affective
Intention
or Action

28. Attitude
• Cognitive component: Represents and
individual’s information and knowledge about an
object. Example of remembering about
Tupperware..
• Affective Component: Summarizes a person’s
overall feeling or emotions towards the object.
Example of tasty food cooked in pressure cooker
• Intention or Action component: It also reflects a
person’s expectation future behavior towards an
object. Example: Purchase intention to buy things
in future

29. Scaling
• Scaling describes the procedures of assigning
numbers to various degrees of opinion,
attitude and other concepts.
• It may be stated here that a scale is a
continuum, consisting of the highest point (in
terms of some characteristic e.g., preference,
favourableness, etc.) and the lowest point
along with several intermediate points
between these two extreme points.

30. Classification of Scales
Single Item vs. Multiple Item Scale.
Comparative vs Non Comparative Scale.

31. Classification of Scales
• Single Item Scale: In the single item scale, there
is only one item to measure a given construct
– How satisfied are you with your current job?
• Very Dissatisfied
• Dissatisfied
• Neutral
• Satisfied
• Very Satisfied
• Other aspects may be left out like, job, pay, work
environment, rules and regulations, security of
job and communication with the seniors.

32. Classification of Scales
• Multiple Item Scale: In this there are many
items that play an important role forming the
underlying construct that the researcher is
trying to measure.
– How satisfied are you with the pay
– How satisfied are you with the rules
– How satisfied are you with the job

33. Classification of Scales
Comparative vs Non Comparative Scale
Scaling Techniques
Comparative
Paired Comparison
Constant Sum
Rank Order
Q-Short and Other
Procedure
Non Comparative
Graphic Rating Scale
Itemized Rating
Scale
Likert
Semantic
Differential
Stapel

34. Comparative Scale
• In comparative scales it is assumed that
respondents make use of a standard frame of
reference before answering the question,
• Example: How do you rate barista in comparison
to cafe coffee Day on Quality of Beverages?
• Please rate Domino's in comparison to Pizza Hut
on basis of your satisfaction level on the 11-point
scale , based on the following parameters: 1-
Extremely poor, 6-Average, 11-Extremely Good.

36. Comparative Scale – Paired
Comparison
• Here a respondent is presented with two objects
and is asked to select one according to whatever
criterion he or she wants to use.
• The Resulting data from this scale is ordinal in
nature.
• Example: Wants to offer chocolate, burger, ice
cream and pizza.
• In general, if there are n items, the number of
paired comparison would be
𝒏(𝒏−𝟏)
𝟐

37. Comparative Scale – Paired
Comparison
• There are many ways of doing it….
• The analysis of paired comparison data would
result in ordinal scale and also interval scale
measurement.
• Example: there are five brands – A,B,C,D and E
and paired comparison with two brands at a time
is presented to the respondent with the option to
chose one of them.
• As there are five brands, it will result in 10 paired
comparison.
• Sample of 250 respondent.

38. A B C D E
A - 0.60 0.30 0.60 0.35
B 0.40 - 0.28 0.70 0.40
C 0.70 0.72 - 0.65 0.10
D 0.40 0.30 0.35 - 0.42
E 0.65 0.60 0.90 0.58 -
A B C D E
A - 1 0 1 0
B 0 - 0 1 0
C 1 1 - 1 0
D 0 0 0 - 0
E 1 1 1 1 -
Total 2 3 1 4 0

39. Comparative Scale – Rank Order Scale
• In Rank order scaling,
respondents are presented
with several objects
simultaneously and asked
to order or rank them
according to some
criterion. Consider, for
example the following
question:
Soft Drink Rank
Coke
Pepsi
Limca
Sprite
Mirinda
Seven up
Fanta

40. Comparative Scale – Constant Sum
Rating Scale
• In this the respondents
are asked to allocate a
total of 100 points
between various
objects and brands. The
respondent distribute
the points to the
various objects in the
order of his preference.
School Points
DPS
Jagran Public
School
Mount Litera
DAV Public School
Jaipuria
Subeam
International
Atulanand
Heritage
Total 100

41. Comparative Scale – Q-Sort
• The Q-sort technique was developed to discriminate
among a large number of objects quickly. This
technique makes use of the rank order procedure in
which objects are sorted into different piles based on
their similarity with respect to certain criterion.
• Example: Group of data can be piled up in five group
– Strongly agree
– Some what agree
– Neutral
– Some what disagree
– Strongly disagree

42. Non Comparative Scales
• In this the respondents do
not make use of any
frame of reference before
answering the questions.
The resulting data is
generally assumed to be
interval or ratio scale.
• The respondent may be
asked to evaluate the
quality of food in a
restaurant on a five point
scale. (1=very poor,
2=poor, and 5=very good)
Non
Comparative
Scales
Graphic Rating
Scales
Itemised Rating
Scale
Likert Rating
Scale
Semantic
differential
Rating Scale
Stapel Rating
Scale

43. Graphic Rating Scale
• This continuous scale, also called graphic
rating Scale. In the graphic rating scale the
respondent is asked to tick mark on the
following question:
Least
Proffered
Most
Preffered

44. Itemized Rating Scale
• The respondent are provided with a scale that
has a number or descriptions associated with
each of the response categories.
• Issues related to the Itemized Rating Scale
– Number of categories to be used.
– Odd or even number of categories.
– Balanced versus unbalanced scales.
– Nature and degree of verbal description.
– Forced Versus non-forced scales.
– Physical form.