An account of the concept of measurement from business research prospective.

1. MEASUREMENT
Chitrasen

2. Introduction
Measurement is ubiquitous in management and an indispensable
concept in research. We will discuss the concept in the perspective of
Business Research.
Problem – Defining what is to be measured, and how to measure it
accurately and reliably.
In business research the things (or concepts) which are inherently
abstract in their nature (e.g. job satisfaction, employee morale, brand loyalty
of consumers) pose some degrees of difficulty for measurement while
concepts which can be assigned numerical values (e.g. sales volume) are
more or less easier.

3. Definitions
Some formal definitions of measurement as proposed by experts
are reproduced below for giving the discussion an academic tone.
Measurement is defined as:“The assignment of numerals to objects or events according to
rules”-Steven
“The assignment of numbers to represent properties”- Campbell
“The assignment of numbers to objects to represent amounts or
degrees of a property possessed by all of the objects- Torgerson.

4. Concept of measurement
In research it is necessary to distinguish between “objects” and
“properties”. Most often their properties and not the objects themselves are the
concerns for measurement. While physical properties may be directly
observed, psychological properties are inferred.
In order for a concept to have the quality of being measurable, it must
first be made operational. An operational definition gives meaning to the
concept by specifying the activities or operations which are necessary and in
turn these can be measured. For example – a satisfied consumer will make at
least five purchases of Product A from Shop T over a three-month period of
time is the operational definition of consumer satisfaction.
However, depending on the context of the research study, it may be
difficult to make operational definitions with ease.

5. Scale
A scale is basically a concept, device, or procedure used in arranging,
measuring, or quantifying events, objects, or phenomenon in any sequence. Scales may
be broadly classified as:
(1) Category scale: Sequence of numbers or words which only serve to
identify certain entities or observations and have no quantitative significance; for
example, a numbered or named list, or numbers on the uniforms of the members of a
team. Also called nominal scale or qualitative scale.
(2) Interval scale: Sequence of numbers in a fixed order representing amount,
interval, or numeric values; for example, a distance or
temperature scale. Also
called quantitative scale.
(3) Sequence scale: Ranked entities or items with a uniform spacing but no
quantitative significance; for example, hardness scale (Moh's scale), pH Scale, or
Richter scale. Also called ordinal scale.
(4) Ratio scale: When a scale consists not only of equidistant points but also
has a meaningful zero point, then we refer to it as a ratio scale. If we ask respondents
their ages, the difference between any two years would always be the same, and „zero‟
signifies the absence of age or birth. Hence, a 100-year old person is indeed twice as
old as a 50-year old one.

6. Levels of measurement
Based on the characteristics of order, distance and origin of the scale
used measurement may be classified in to four different levels as under:
(1)
Nominal measurement
(2)
Ordinal measurement
(3)
Interval measurement
(4)
Ratio measurement

7. Nominal measurement
A nominal measurement is the simplest of the four and in which the
numbers or letters assigned to objects serve as labels for identification or
classification.
Example:
Males = 1, Females = 2
Sales Zone A = Islamabad, Sales Zone B = Rawalpindi
Drink A = Pepsi Cola, Drink B = 7-Up, Drink C = Miranda

8. Ordinal measurement
An ordinal measurement is one that arranges objects or alternatives
according to their magnitude.
Examples:
Career Opportunities = Moderate, Good, Excellent
Investment Climate = Bad, inadequate, fair, good, very good
Merit = A grade, B grade, C grade, D grade
A problem with ordinal scales is that the difference between
categories on the scale is hard to quantify, i.e. excellent is better than good
but how much is excellent better?

9. Interval measurement
An interval level of measurement is one that not only arranges
objects or alternatives according to their respective magnitudes, but also
distinguishes this ordered arrangement in units of equal intervals. Interval
scales indicate order (as in ordinal scales) and also the distance in the order.
Examples:
Consumer Price Index
Temperature Scale in Fahrenheit
Interval scales allow comparisons of the differences of magnitude (e.g. of
attitudes) but do not allow determinations of the actual strength of the
magnitude. The criteria lacking is the origin or zero point.

10. Ratio measurement
A ratio level measurement is one that possesses absolute rather than
relative qualities and has an absolute zero.
Examples:
Money
Weight
Distance
Temperature on the Kelvin Scale
Ratio scales are the most sophisticated of scales, since it
incorporates all the characteristics of nominal (definition of nominal scale),
ordinal (definition of ordinal scale) and interval scales (definition of interval
scale). As a result, a large number of descriptive calculations are applicable.

11. Characteristics of measurement levels
Level of
measuremen
t
Characteristics
Descriptive Statistics
Nominal
No, Order, Distance
or Origin
Frequency in each category,
percentage in each category, mode
Ordinal
Order but not
distance or origin
Median, range, percentile ranking
Interval
Both order and
Mean, standard deviation, variance
distance but no origin
Ratio
Order, Distance and
Origin
Geometric mean, coefficient of
variation

12. Characteristics of good measurement
Uni-dimensionality: Measurement should not measure more than one
characteristic at a time. Example: scale should not measure length and temperature at
the same time.
Linearity: A good measurement should follow the straight line model.
Validity: The measurement scale should measure what it is supposed to
measure.
Reliability: This refers to consistency. The measurement should give
consistent result.
Accuracy and precision:
It should give an accurate and precise
measure of what is being measured.
Simplicity :Measurement tool should not be very complicated or over
elaborate.
Practicability:
The tool should be easy to understand administer.

13. Validity
Validity is an important criteria for a measurement and required to
be established in research. A scale may be considered valid if it
effectively measures a specific property or characteristic that it intends to
measure. Validity is not a problem while measuring physical
characteristics like length, weight and height etc. but for abstract
characteristics such as attitude and motivation which are measured
indirectly. In such measurement evidence of validity are required.
Validity is classified in to following three types and degree of
validity of each type can be determined using logic, statistical technique
or both.
Content validity
Predictive validity
Construct validity

14. Content Validity
Content validity is also of two types- (1) Face validity (2) Sampling
validity.
Face validity is determined through a subjective evaluation of a
measuring scale.
Example: a researcher may develop a scale to measure consumer
attitude towards a brand and pre test the scale among a few experts. If the
experts are satisfied with the scale the researcher may conclude face validity
of the scale.
Sample validity refers to how representative the content of
measuring instrument is? Measuring instrument‟s content should be
representative of content universe of the characteristic being measured.
Example: if attitude is the characteristic being measured, its content
universe may comprise statements and questions indicating all aspects of
attitude and the sampling validity can be determined by comparing these with
the content of the measuring instrument.

15. Criterion related Validity
Criterion-related validity usually includes any validity strategies that focus on the
correlation of the test being validated with some well-respected outside measure(s) of the same
objectives or specifications. Example: if a group of testers were trying to develop a test for business
English to be administered primarily in Japan and Korea, they might decide to administer their new
test and the TOEIC® (Test of English for International Communication )to a fairly large group of
students and then calculate the degree of correlation between the two tests. If the correlation
coefficient between the new test and the TOEIC turned out to be high, that would indicate that the
new test was arranging the students along a continuum of proficiency levels very much like the
TOEIC does. The result could be used to support the validity of the new test.
Criterion-related validity of this sort is sometimes called concurrent validity (because
both tests are administered at about the same time). Another version of criterion-related validity is
called predictive validity. Predictive validity is the degree of correlation between the scores on a test
and some other measure that the test is designed to predict. Example: a number of studies have
been conducted to examine the degree of relationship between students' Graduate Record
Examination® (GRE) scores and their grade point averages (GPA) after two years of graduate
study. The correlation between these two variables represents the degree to which the GRE
predicts academic achievement as measured by two years of GPA in graduate school.

16. Construct Validity
A construct, or psychological construct is an attribute, proficiency, ability, or skill of the
human brain and is defined by established theories. For example, "overall English language
proficiency" is a construct. It exists in theory and has been observed to exist in practice.
Construct validity has traditionally been defined as the experimental demonstration that
a test is measuring the construct it claims to be measuring.
Example: in a differential-groups study the performances on the test are compared for
two groups (1) one that has the construct and (2)that does not have the construct. If the group with
the construct performs better than the group without the construct it may provide evidence of the
construct validity of the test.
Alternatively in an intervention study, wherein a group that is weak in the construct is
measured using the test, then taught the construct, and measured again. If a significant difference
is found between the pretest and posttest, it can support the construct validity of the test.
The concept of construct validity is very well accepted. A contemporary view is that all
three types of validity (content, criterion-related, and construct validity) are different facets of a
single unified form of construct validity.

17. Reliability
Reliability refers to the ability of the scale to provide consistent and accurate
results. The two dimension of reliability are stability and equivalence(non-variability).
Stability refers to consistency of results with repeated measurements. Example: an
weighing machine may be said to be reliable if the same reading is given every time the
same object is weighed. Equivalence refers to consistency at a given point of time among
different investigators and samples of items.
Reliability can be improved in three ways:(1) By reducing the external source of variation
(2) By making the instrument more consistent internally
(3) By adding more number of item to the measuring instrument.
Standardizing the conditions under which measurement takes place, employing
trained investigators, providing standard instructions, analyzing the items in the
instrument for consistency and adding more number of items will increase the
probability of accurate measurement.

18. Reliability vs validity
Reliability and validity are closely interlinked.
A measurement instrument that is valid is always reliable but the
reverse may not true. That is, an instrument that is reliable is not always
valid.
However an instrument that is not valid may or may not be reliable
but an instrument that is not reliable is never valid.

19. Complex measurement
When a concept is simple, it can be measured easily usually with one question or
observation. Example: To what extent do consumers of Product X like the product‟s packaging
material? (very much, somewhat, not at all).
However, when the concept to be measured is complex and abstract, two or more
questions or observations may be required in order to get accurate data. Example: The level of
a salesperson‟s motivation depends on (1) job satisfaction (2) workplace environment (3) family
life. Its measurement will require multiple questions or observations. Indexes (or composite
measures) are meant to deal with the issue of multi-dimensionality (e.g. an index of social class
may have the variables residence, occupation and education).

20. Measurement techniques
Common techniques used in business research include rating, ranking, sorting and the
choice technique.
Rating is frequently employed in business research and many scales have been
developed for this purpose like:
Simple Attitude Scales, Category Scales, Likert Scale,
Semantic Differential, Numerical Scales, Constant-Sum Scale, Stapel Scale and Graphic Scales.
Brief account on important few are as under:In attitude scaling, individuals are typically asked whether they agree or disagree with a question (or
questions). Simple attitude scales have the properties of a nominal scale and they do not permit fine distinctions in the
respondents‟ answers.
A category scale consists of several response categories to provide the respondent with alternative ratings.
Category scales are more sensitive than rating scales.
A likert Scale is a measure of attitudes designed to allow respondents to indicate how strongly they agree or
disagree with carefully constructed statements that range from very positive to very negative towards an object or subject. A
Likert Scale may include a number of question items, each covering some aspect of the respondent‟s attitude, and these
items collectively form an index.
The semantic differential is an attitude measuring technique that which consists of a series of seven bi-polar
rating scales which allow response to a concept (e.g. organization, product, service, job). An advantage of the semantic
differential is its versatility, on the other hand, it uses extremes which may influence respondents‟ answers.
The Behavioural Differential: This is an instrument for measuring the behavioural intentions of subjects
towards an object or category of objects.

21. Conclusion
21
Measurement is an important concept in research and perhaps the
most difficult part. It facilitates verification of hypotheses, helps to quantify
variables, makes data suitable for statitical analysis and enables
comparision of objects in terms of specific characteristics. What can be
measured can be solved- a saying on problem solving shows the
importance of the concept in management as a whole.
The classical concept of quantity can be traced back to John Wallis
and Isaac Newton, and was foreshadowed in Euclid's Elements. However
among the representational theories contribution of psychologist Stanley
Smith Stevens „s „The theory of scale of measurement‟ is note worthy from
perspective of research in social sciences and business. However concept
is still developing and more robust scales and analysis techniques are
being added with time.