2. Contents
Measurement and Scaling
What Do We Measure
Measurement Scales
Nominal
Ordinal
Interval
Ratio
Scaling Methods
Comparative
Non-Comparative
Reliability and Validity
Primary and Secondary Sources of
Data
3. 01
Step 1
Selecting observable
empirical events
02
Step 2
Developing a set of
mapping rules
03
Step 3
Applying the mapping rules to
each observation of that event
Measurement and Scaling
Measurement in research consists of assigning numbers to empirical
events,objects or properties or activities in compliance with a set of rules.
This definition implies that measurement is a three step process :
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4. What Do We Measure?
Variables being studied in
research may be classified
into objects or properties.
Objects
Tangible Items like people, automobiles or
furniture
Things that are not as concrete like genes,
peer pressure
They include concepts of ordinary experiences:
Properties
Physical like weight, height, posture
Psychological like attitude, intelligence
Social like leadership ability, status
They inculde characteristics of the objects:
For Example - Properties of a person are
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5. Measurement
Scales
In measuring one devises some
mapping rules and then translates
the observation of property indicants
using this rule.Each one has its own
set of underlying assumptions about
how the numerical symbols
corresponds to real-world
observations.
Nominal Ordinal
Interval Ratio
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6. Nominal Scale
A nominal scale is a scale of measurement used to assign events or objects into
discrete categories.
This form of scale does not require the use of numeric values or categories
ranked by class, but simply unique identifiers to label each distinct category.
Often regarded as the most basic form of measurement, nominal scales are
used to categorize and analyze data in many disciplines.
From a statistics point of view this scale is one of the easiest to understand
measurement scale. It is assigned to items that are not quantitative or number
oriented.
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7. Examples of Nominal Scale
Illustration 1
Illustration 2
Illustration 3
Let’s assume we have 5 colors, orange, blue, red, black and yellow. We could
number them in any order we like either 1 to 5 or 5 to 1 in ascending or descending
order. Here numbers are assigned to colors only to identify them.
In the case of a gender scale, an individual can be categorized either as male or
female. In this case, all objects in the category will have the same number, for
example, all males can be no. 1 and all females can be no. 2. Please note, that
nominal is purely used for counting purposes.
Another example of nominal scale from a research activity point to view is YES/NO
scale. It essentially has no order.
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8. Ordinal Scale
Ordinal scale is the 2nd level of measurement that reports the ranking and
ordering of the data without actually establishing the degree of variation
between them.
“Ordinal” indicates “order”. Ordinal data is quantitative data which have
naturally occurring orders and the difference between is unknown. It can be
named, grouped and also ranked.
An ordinal scale is used as a comparison parameter to understand whether the
variables are greater or lesser than one another using sorting. The central
tendency of the ordinal scale is Median..
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9. Examples of Ordinal Scale
Survey respondents will choose between these options of satisfaction but the
answer to “how much?” will remain unanswered.
Likert Scale is an example of why the interval difference between ordinal
variables cannot be concluded. In this scale the answer options are usually
polar such as, “Totally satisfied” to “Totally dissatisfied”.
The intensity of difference between these options can’t be related to specific
values as the difference value between totally satisfied and totally dissatisfied
will be much larger than the difference between satisfied and neutral.
“How satisfied are you with our products?"
1- Totally Satisfied 2- Satisfied 3- Neutral 4- Dissatisfied 5- Totally Dissatisfied
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10. Interval Scale
The interval scale is a quantitative measurement scale where there is order, the
difference between the two variables is meaningful and equal, and the
presence of zero is arbitrary.
It measures variables that exist along a common scale at equal intervals. The
measures used to calculate the distance between the variables are highly
reliable.
The interval scale is preferred to nominal scale or ordinal scale because the
latter two are qualitative scales. The interval scale is quantitative in the sense
that it can quantify the difference between values.
This is a preferred scale in statistics because you can assign a numerical value to
any arbitrary assessment, such as feelings and sentiments.
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11. Examples of Interval Scale
Illustration 1
Illustration 2
Some examples of interval scale are Likert Scale, Bipolar Matrix and Net
Promoter Score (NPS)
For instance, for an IQ test, one may use an interval scale. The use of an interval
scale can help measure that the difference between an IQ of 80 and 90 is the same
as an IQ of 90 and 100. At the same time, the score cannot be zero, because the
minimum level for IQ is 40.
The scale may show the value as zero but it does not mean true zero or absence.
For example, in the case of temperature, the indication for zero degrees Fahrenheit
and Celsius does not mean the absence of temperature. The value is used to
describe the phenomena where equal parts of ice, water, and salt freeze.
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12. Ratio Scale
Ratio scale is a type of variable measurement scale which is quantitative in
nature. It allows any researcher to compare the intervals or differences. Ratio
scale possesses a zero point or character of origin.
It is the most informative scale as it tends to tell about the order and number of
the object between the values of the scale. The most common examples of this
scale are height, money, age, weight etc.
In this scale, variables can be systematically added, subtracted, multiplied and
divided (ratio). All statistical analysis including mean, mode, the median can be
calculated using it.
Ratio scale has units which have several unique and useful properties. One of
them is they allow unit conversion.
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13. Examples of Ratio Scale
Less than 5 ft
5 ft 1 in - 5 ft 5 in
5 ft 6 in - 6 ft
More than 6 ft
Take an example of calculation of energy flow. Several units of energy occur like
Joules, gram-calories, kilogram-calories, British thermal units. Still more units of
energy per unit time (power) exist kilocalories per day, liters of oxygen per hour,
ergs, and Watts. This conversion of units is made possible by ratio scale.
However, the temperature cannot be measured on this scale because zero
degree celsius doesn’t mean there is no cold or heat for that matter. But most of
the scientific variables can be measured on this scale.
“What is your height in feet and inches?"
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14. Scaling Methods
Comparative
Methods
Paired Comparison
Rank Order
Constant Sum
For comparing two or more variables, a
comparative scale is used by the
respondents. Following are the different
types of comparative scaling techniques:
Likert Scale
Semantic Differential Scale
Stapel Scale
A non-comparative scale is used to
analyse the performance of an individual
product or object on different
parameters. Following are some of its
most common types:
Non-Comparative
Methods
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15. 01
Paired Comparison
A paired comparison
symbolizes two variables
from which the respondent
needs to select one.
02
Rank Order
In rank order scaling
the respondent needs
to rank or arrange the
given objects
according to his or
her preference.
03
Constant Sum
It is a scaling technique where
a continual sum of units like
points, is given to the features,
attributes and importance of a
particular product or service by
the respondents.
Comparative Methods
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16. Paired Comparison Scale
The Paired Comparison Scaling is a comparative scaling technique wherein the
respondent is shown two objects at the same time and is asked to select one
according to the defined criterion. The resulting data are ordinal in nature.
It is often used when the stimulus objects are physical products. The
comparison data so obtained can be analyzed.
The paired comparison method is effective when the number of objects is
limited because it requires the direct comparison, and with a large number of
stimulus objects the comparison becomes cumbersome.
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17. Examples of Paired Comparison Scale
Fig: Comparing employees with each other for performance appraisal by using paired
comparison scaling method
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18. Rank Order Scale
The Rank Order Scaling is a yet another comparative scaling technique wherein
the respondents are presented with numerous objects simultaneously and are
required to order or rank these according to some specified criterion.
It is one of the most commonly used survey question types when the market
researcher would like to understand the order of importance of items when
there are multiple items.
The ranking is by a simple ordinal position where one variable or option is
higher than another or a relative position where one variable has a higher
relative rating than another. It could be on the basis of features, needs, likes or
dislikes, effectiveness, etc.
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20. Constant Sum Scale
A constant sum scale is a type of question used in a market research survey in
which respondents are required to divide a specific number of points or
percents as part of a total sum. The allocation of points are divided to detail the
variance and weight of each category.
Constant sum scales are a less frequently used question in surveys when
compared to basic likert scales, single radio responses, or checklists. They are an
excellent way to create variance among a data set and truly understand which
factors are key and which are not for customers or respondents.
They are especially helpful if you need to ask a question to a customer or
respondent where you believe several factors are critical or of high importance.
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22. 01
Likert Scale
The researcher provides
some statements and ask
the respondents to mark
their level of agreement or
disagreement over these
statements by selecting any
one of the options from the
five or seven given
alternatives.
02
Semantic
Differential Scale
A bi-polar non-comparative
rating scale is where the
respondent can mark on any
of the points for each given
attribute of the object as per
personal choice.
03
Stapel Scale
It is that itemized rating scale
which measures the response,
perception or attitude of the
respondents for a particular
object through a unipolar
rating.
Non-Comparative Methods
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23. Likert Scale
A Likert scale is a unidimensional scale that researchers use to collect
respondents’ attitudes and opinions. Researchers often use this psychometric
scale to understand the views and perspectives towards a brand, product, or
target market.
Items are easily related to the sentence’s answers, regardless of whether the
relationship between item and sentence is evident. Also, the items have two
extreme positions and an intermediate answer option that serves as graduation
between the extremes.
It is better to use a scale as wide as possible like a 5 point or a 7 point scale
because one can always collapse the answers into concise groups, if
appropriate, for analysis.
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25. Semantic Differential Scale
A semantic differential scale is a survey or questionnaire rating scale that asks
people to rate a product, company, brand, or any 'entity' within the frames of a
multi-point rating option.
These survey answering options are grammatically on opposite adjectives at
each end.
Respondents can express their opinions about the matter in hand more
accurately and entirely due to the polar options provided in the semantic
differential. The researcher declares a statement and expects respondents to
either agree or disagree with that.
For example, love-hate, satisfied-unsatisfied, and likely to return-unlikely to
return with intermediate options in between.
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27. Stapel Scale
Stapel scale is defined as a rating scale that is close-ended with a single
adjective (unipolar), developed to gather respondent insights about a particular
subject or event. The survey question is comprised of an even number of
response options without a neutral point.
The stapel scale can be indicated as standalone questions or even as a matrix
question type where each line item is one unique adjective.
Since this is a non-comparative and categorical scale, it makes it very similar to
a semantic differential scale with the sole difference being the presence of only
adjective in between a positive and negative category rather than having two
opposing adjectives.
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29. Reliability
Research reliability is the degree to
which research method produces
stable and consistent results. A
specific measure is considered to be
reliable if its application on the same
object of measurement number of
times produces the same results.
For Example:
Think of a scale to measure weight. You
would expect this scale to be consistent
from one time to the next. If you stepped
on the scale and it read 140 pounds, then
got off and back on, you would expect it
to again read 140. If it read 110 the second
time, the scale would not be reliable.
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30. Validity
Validity is the accuracy of a measure or the extent to which
a score truthfully represents a concept. In other words, are
we accurately measuring what we think we are
measuring?
01
Content Validity
It refers to the degree that a
measure covers the domain of
interest. Do the items capture the
entire scope, but not go beyond,
the concept we are measuring?
02
Criterion Validity
It is the ability of a measure to
correlate with other standard
measures of similar constructs
or established criteria. It
addresses the question, “How
well does my measure work in
practice?”
03
Construct Validity
It exists when a measure reliably
measures and truthfully
represents a unique concept. It
is a great way of ensuring that
the measurement method
aligns well with the construct
that you aim to measure.
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31. Primary Data
Primary Data refers to the first hand data
gathered by the researcher himself Primary
data are usually collected from the source—
where the data originally originates from and
are regarded as the best kind of data in
research.
Direct personal investigation
Indirect oral investigation
Information through
correspondents
Telephonic interview
Mailed questionnaire
The questionnaire filled by
enumerators
Sources of Data
Collection
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32. Secondary Data
Secondary data is the data that has already
been collected through primary sources and
made readily available for researchers to use for
their own research. It is a type of data that has
already been collected in the past.
Books
Published Sources
Unpublished Personal
Sources
Journals
Newspapers
Blogs
Government Records
Sources of Data
Collection
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