Measurement Scales for Attributes

Measurement of Scaling
Dr. Dhobale J V
Assistant Professor
IBS, IFHE, Hyderabad.
IBS Hyderabad 1
Business Research Methods (SHRM-431)
Chapter No.-07

Objectives
 List the four scales (levels) of measurement.
 Criteria for good measurement
 Explain the concept of validity as it relates to
measuring instruments.
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Scales of Measurement
 Measurement consists of “rules for assigning
numbers to objects in such a way as to
represent quantities of attributes.”
 We measure the attributes of objects and not
the objects.
 How the numbers will be assigned?
 The first step in measuring some attribute is to
determine the properties of the attribute.
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 Measurement
 The numbering system is simply a tool that
must be used correctly so that we don't
mislead ourselves or our clients.
 Measurement of psychological properties of a
phenomenon, like motivation
 Ex. Strongly motivated 5
Motivated 4
Neutral 3
Not Motivated 2
Not Strongly motivated 1 4IBS Hyderabad

 There are four types of scales used to
measure attributes of objects:-
1. Nominal Scale
2. Ordinal Scale
3. Interval Scale
4. Ratio Scale
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1. Nominal Scale –
 These numbers simply identify the individual
or object that has been assigned the number.
 In these examples, the numbers are used
to uniquely identify individuals, but nominal
scales can also be used to categorize people
or things into groups based on their attributes.
 With nominal scales, the numbers don't mean
anything other than simple individual or
category identification.
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 One of the most basic uses of numbers is
to identify or categorize particular objects.
 Ex- A person's social security number is
a nominal scale .
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 The numbers we assign to represent the
groups don't have any meaning beyond
identification.
 We could actually use any numbers we want,
because the numbers don't matter.
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2. Ordinal Scale –
 With a nominal scale, the numbers assigned to
individuals or categories are arbitrary.
 They can be changed or reversed—it doesn't
matter as long as we know which number
represents each category. This works because
the numbers don't imply any order to the
attributes you're trying to measure.
 Measurement in which numbers are assigned
to data on the basis of some order (e.g., more
than, greater than) of the objects.
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 Can you put these soft drinks in order from
least preferred to most preferred? Having a
preference implies that you can order objects.
 With an ordinal scale , we could say that the
number 2 is greater than the number 1, that 3
is greater than both 2 and 1, and that 4 is
greater than all three of these numbers.
 The numbers 1, 2, 3, and 4 are ordered, and in
this case the larger the number, the greater
the property.
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 You could also set up your ordinal scale so
that lower numbers reflect more of the
property; the important point is that there is a
consistent order in whatever numbers you
assign.
 With an ordinal scale, the numbers have more
meaning in that they represent this relative
standing. But they don't go further than that;
we know that one option has more of some
attribute than some other option, but we don't
know how much more. 12IBS Hyderabad

 The nominal and ordinal level data
measurements are often used for imprecise
measurements such demographic questions,
ranking of items under the study, and so on.
 This is why these data are termed as non-
metric data and referred as qualitative data.
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2. Ordinal Scale–
 Whether we can use the ordinal scale to
assign numbers to objects depends on the
attribute in question.
 The attribute itself must possess the ordinal
property to allow ordinal scaling that is
meaningful.
 Note that it is impossible to say how much any
individual respondent preferred one object to
another; all we can say is that one is preferred
over the other.
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3. Interval Scale –
 The intervals between the numbers tell us how
far apart the objects are with respect to the
attribute.
 This tells us that the differences can be
compared. The difference between 1 and 2 is
equal to the difference between 2 and 3.
 Measurement in which the assigned numbers
legitimately allow the comparison of the size of
the differences among and between members.
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 In the soft-drink example we're asking the
respondents to rate their attitudes toward the
brands, using 1–7 scales where 1 = “extremely
unfavorable” and 7 = “extremely favorable.”
 This interval scale will allow us to see the
relative strength of a respondent's feelings
toward each of the soft drinks.
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 Respondents can indicate the full range of
possible attitudes toward each brand, a big
step forward from simply knowing the order of
preference.
 Thus, interval scales allow us to say that one
brand is preferred over another by comparing
scores and, even better, to say whether the
brand is generally liked or disliked.
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 We can't compare the absolute magnitude of
numbers when measurement is made on the
basis of an interval scale.
 The reason is that on an interval scale, the
zero point is established arbitrarily.
 Is there such a thing as having zero attitude
toward some object? Attitudes may be
negative or positive or neutral (or nonexistent
for unknown objects), but there is no obvious
point at which attitude is equal to zero.
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 With an interval scale, you can
calculate mean scores or averages on
measures in addition to median and mode
scores.
 The mean is “meaningful” for interval scales
because of the equal intervals between scale
positions.
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4. Ratio Scale –
 Measurement that has a natural, or absolute,
zero and therefore allows the comparison of
absolute magnitudes of the numbers.
 Ex- Height and weight.
 If a person was completing the final item in
example and reported consuming 20 servings
of Mountain Dew and only five servings of
Sprite, he has consumed four times as much
Mountain Dew—an indication of a strong
preference for Mountain Dew versus Sprite.
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4. Ratio Scale –
 With a ratio scale we can compare intervals,
rank objects according to magnitude, or use
the numbers to identify the objects.
 As well as the more usual arithmetic mean,
median, and mode, is a meaningful measure
of average when attributes are measured on
a ratio scale.
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4. Ratio Scale –
 Researchers should use ratio scales for
measuring these sorts of attributes whenever
possible, unless there is a compelling reason
not to do so.
 The Interval & Ratio scale data are collected
using some precise instruments.
 These data are called metric data and are
sometimes refereed as quantitative data.
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Level of Measurement
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Other Considerations in Designing
Scales
 There are a number of issues that must also
be considered when designing scales for
measuring –
1. Number of Items in a Scale
2. Number of Scale Positions
3. Including a “Don't Know” or “Not Applicable”
Response Category
4. What Is a Construct?
5. Type of Data and Possible Analysis with Such
Data
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Scales
1. Number of Items in a Scale-
 Global Measure - A measure designed
to provide an overall assessment of an
object or phenomenon, typically using
one or two items.
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Scales
 Composite Measure - A measure
designed to provide a comprehensive
assessment of an object or
phenomenon, with items to assess all
relevant aspects or dimensions.
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Scales
 Composite measure, consisting of
measures of satisfaction with the
location, product selection, prices,
employees, and so on, would allow the
managers to more easily diagnose any
problem areas.
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Scales
2. Number of Scale Positions - For most
purposes, a minimum of five response
categories should be included.
 There's really no need to go beyond 9
or 10 scale positions.
 Both even and odd numbers are used
regularly in practice.
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Scales
3. Including a “Don't Know” or “Not
Applicable” Response Category-
 Research has indicated that no opinion
options are more frequently chosen
1. By individuals with lower levels of
education.
2. By those answering anonymously.
3. For questions that appear later in a survey.
4. By respondents who indicated that they
had devoted less effort to the task of
completing a survey. 29IBS Hyderabad

Scales
4. What Is a Construct? –
 Constructs are complex abstractions.
 These are subjective in nature and are
underlying dimensions, representing the
implicit dimensions of concepts, making
it difficult to measure them.
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Scales
4. What Is a Construct? –
 Ex - If loyalty is measured by three
indicator variables, namely,
1. The number of times the respondent has
visited a store.
2. Amount purchased from a store.
3. Number of people to whom the store was
recommended.
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Scales
5. Type of Data and Possible Analysis
with Such Data –
 Statistical analysis would mean that the
levels of measurement are metric.
 We have two variables in the metric
scale, either interval or ratio scale.
 The mean and standard deviation of
data would mean the central tendency
and dispersion of that data.
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Establishing the Validity and Reliability
of Measures
 Validity - Almost nothing in research can be
measured without error.
 Observation produces the most accurate
measures - but most of the things we want to
measure in research simply can't be observed.
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of Measures
 Validity - Your observed response (i.e., the
number you circled) is a combination of your
true position on the issue plus any kinds of
error that have influenced your response.
 Our goal is to minimize error. As systematic
and random errors decrease, the validity of the
measure increases.
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of Measures
 Validity -
1. Systematic error , which is also called
constant error, is error that affects the
measurement in a constant way.
 Ex. Individuals willingness to express negative
feelings or maybe tend toward more positive
answers which can introduce systematic
errors.
 Differences in how surveys are administered
can also introduce systematic error into a
project.
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of Measures
 Validity -
2. Random error - is due to temporary aspects
of the person or measurement situation; this
can affect the measurement in irregular ways.
 Random error is present when we repeat a
measurement on an individual and don't get
the same scores as the first time we did the
measurement, even though the characteristic
being measured hasn't changed.
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of Measures
 Validity -
2. Random error -
 Your mood, state of health, fatigue, and so
forth might have affected your response to the
question about General Electric, yet these
factors are temporary.
 If you've had a hard day, your answer was
probably more negative than if you'd read this
section tomorrow.
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of Measures
 Validity -
2. Random error -
 The situation surrounding the measurement
also affects the score in random ways.
 Differences in the resulting scores may have
more to do with interpretation than with true
differences in the characteristic we wanted to
measure.
 One of your main tasks is to write items or
questions that mean the same thing to all
respondents.
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of Measures
 Validity -
2. Random error -
 the higher the levels of systematic and random
error, the lower the validity , or correctness, of
a measure.
 If we know the source of the systematic error,
sometimes we can adjust for it.
 Random error, on the other hand, is just that—
random—and we can't hold it constant or
account for it statistically.
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of Measures
 Reliability - Reliability refers to the ability of
a measure to obtain consistent scores for the
same variable or concept across time, across
different evaluators, or across the items
forming the measure.
 Consistency is the hallmark of reliability; as a
result, improving reliability requires decreasing
random error.
 A reliable measure is just consistent—it may
not be measuring the right thing, but it returns
consistent scores.
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of Measures
 Illustration of Difference between
Reliability and Validity
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Relationship between Measurement
Error and Scaling Concepts
 Relationship between measurement error and
scaling is given as –
 Where XO is observed score or
measurement, XT is true score of the
characteristic, XS is systematic error, and XR is
random error.
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Relationship between Measurement
Error and Scaling Concepts
 Sources that can cause systematic error are –
 Social desirability, Cross-cultural differences,
Personality differences, Seasonality, etc.
 Sources of random errors are –
 Respondents' mood, Question ambiguity,
Respondent's fatigue, etc.
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Reviews
 We have discussed -
 List the four scales (levels) of measurement.
 Criteria for good measurement
 Explain the concept of validity as it relates to
measuring instruments.
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References
 MARKETING RESEARCH – A SOUTH ASIAN
PERSPECTIVE by Brown, Sutter , Adhikari,
Cengage Learning, India.
 Business Research Methods - Donald R
Cooper, Pamela S Schindler, J K Sharma,
MCGraw Hill Education
 Business Research Methods - Naval Bajpai,
Pearson Education, India.
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Measurement Scales for Attributes

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