The document discusses key concepts in measurement and scaling for business research. It covers determining what to measure based on the research question, different levels of scale measurement (nominal, ordinal, interval, ratio), how to operationalize concepts, methods for assessing reliability and validity of measures, and how to develop composite or index measures. The goal is to understand how to properly measure concepts both concretely and abstractly for research purposes.

1. Chapter 13
Measurement and
Scaling Concepts
BUSINESS
RESEARCH
METHODS

2. 13–2
LEARNING OUTCOMES
1. Determine what needs to be measured to address a
research question or hypothesis
2. Distinguish levels of scale measurement
3. Know how to form an index or composite measure
4. List the three criteria for good measurement
5. Perform a basic assessment of scale reliability and
validity
After studying this chapter, you should be able to

3. 13–3
What Do I Measure?
• Measurement
 The process of describing some property of a
phenomenon, usually by assigning numbers in a
reliable and valid way.
 Measurement means assigning numbers or other
symbols to characteristics of objects according to
certain prespecified rules.
 The decision statement, corresponding research
questions, and research hypotheses can be used to
decide what concepts need to be measured.
 The numbers convey information about the property being
measured.
 When numbers are used, the researcher must have a rule for
assigning a number to an observation in a way that provides
an accurate description.

4. • Numbers are usually assigned for two reasons:
 First, numbers permit statistical analysis of the
resulting data
 Second, numbers facilitate the communication of
measurement rules and results
• Employee Satisfaction Level
• (1-----agree 3----Neutral 5 ----disagree)
 Absenteeism 1 2 3 4 5
 Meet deadlines 1 2 3 4 5
 Punctual 1 2 3 4 5
 Results 1 2 3 4 5
13–4
What Do I Measure?

5. Measurement Example
• Measurement can be illustrated by thinking about the
way instructors assign students’ grades
 A — Represents excellent performance
 B — Represents good performance
 C — Represents average performance
 D — Represents poor performance
 F — Represents failing performance
• A student can be assigned a grade corresponding to a
percentage performance scale.
 90 -100 percent — Represents a perfect score. A Grade
 80 – 89 percent – Represents B Grade
 70–79 percent — Represents differing degrees of passing
performance, each number representing the proportion of
correct work. C Grade
 60–69 percent — Represents failing performance but still
captures proportion of correct work. D Grade 13–5

6. 13–6
EXHIBIT 13.2 Are There Any Validity Issues with this Measurement?

7. • Concepts
 A generalized idea that represents something of
meaning
 A researcher has to know what to measure before knowing
how to measure something.
 The problem definition process should suggest the concepts
that must be measured.
 Concepts can be concrete and abstract
 Concepts such as age, gender, education, and
number of children are relatively concrete properties.
 Concepts such as loyalty, personality, channel power,
trust, corporate culture, customer satisfaction, value,
and so on are abstract and more difficult to both
define and measure
13–7
What Do I Measure?

8. 13–8
Operational Definitions
• Operationalization
 The process of identifying scales that correspond to
variance in a concept involved in a research process.
• Scales
 A scale provides a range of values that correspond to
different characteristics or amounts of a characteristic
exhibited in observing a concept.
• Correspondence rules
 Indicate the way that a certain value on a scale
corresponds to some true value of a concept.
 An example of a correspondence rule: “Assign the numbers 1
through 7 according to how much trust that you have in your sales
representative. If the sales representative is perceived as completely
untrustworthy, assign the numeral 1, if the sales rep is completely
trustworthy, assign a 7.”

9. 13–9
Operational Definitions (cont’d)
• Variable
 Anything that varies or changes from one instance to
another; can exhibit differences in value, usually in
magnitude or strength, or in direction.
 Capture different values of a concept.
 Consider the following hypothesis:
 H1: Experience is positively related to job performance.
 The hypothesis implies a relationship between two variables,
experience and job performance.
• Constructs
 Concepts measured with multiple variables.
 Sometimes, a single variable cannot capture a concept alone.
Using multiple variables to measure one concept can often
provide a more complete account of some concept than could
any single variable.

10. 13–10
Levels of Scale Measurement
• Nominal
 Nominal scales are used for labeling variables,
without any quantitative value. “Nominal” scales
could simply be called “labels.”
 Assigns a value to an object for identification or
classification purposes.
 Most elementary level of measurement.
• Ordinal---Rank-Order
 Ranking scales allowing things to be arranged based
on how much of some concept they possible.
 Have nominal properties.
 Ordinal scales are typically measures of non-numeric
concepts like satisfaction, happiness, discomfort, etc.

11. 13–11
Levels of Scale Measurement (cont’d)
• Interval
 Capture information about differences in quantities of
a concept.
 Have both nominal and ordinal properties.
 Variables that have familiar, constant, and computable
differences are classified using the Interval scale.
• Ratio
 Highest form of measurement.
 Have all the properties of interval scales with the
additional attribute of representing absolute
quantities.
 Examples of variables which are ratio scaled include weights,
lengths and times

12. 13–12
EXHIBIT 13.4 Nominal, Ordinal, Interval, and Ratio Scales Provide Different Information

13. Nominal Scale Examples
13–13

14. Ordinal Scale Example
13–14

15. Interval Scale Example
• What is your family income?
 $0 to 10,000
 $10,000 to 20,000
 $20,000 to 30,000
 More than $30,000
• Heat measured in degrees centigrade.
13–15

16. Ratio Scale Example
13–16

17. 13–17
EXHIBIT 13.5 Facts About the Four Levels of Scales

18. 13–18
Mathematical and Statistical Analysis of Scales
• Discrete Measures
 Measures that can take on only one of a finite number
of values.
 Discrete variables represent counts (e.g. the number of
objects in a collection)
• Continuous Measures
 Measures that reflect the intensity of a concept by
assigning values that can take on any value along
some scale range.
 Continuous variables represent measurable amounts (e.g.
water volume or weight)

19. 13–19
Index Measures
• Attributes
 Single characteristics or fundamental features that
pertain to an object, person, or issue.
• Index Measures
 Assign a value based on how much of the concept
being measured is associated with an observation.
 Indexes often are formed by putting several variables
together.
 Example of Socioeconomic status
• Composite Measures
 Assign a value to an observation based on a
mathematical derivation of multiple variables.

20. 13–20
Computing Scale Values
• Summated Scale
 A scale created by simply summing (adding together)
the response to each item making up the composite
measure.
• Reverse Coding
 Means that the value assigned for a response is
treated oppositely from the other items.

21. 13–21
EXHIBIT 13.6 Computing a Composite Scale

22. 13–22
Three Criteria for Good Measurement
Sensitivity
Reliability Validity
Good
Measurement

23. 13–23
Reliability
• Reliability
 The degree to which measures are free from random
error and therefore yield consistent results.
 An indicator of a measure’s internal consistency.
• Internal Consistency
 Represents a measure’s homogeneity or the extent to
which each indicator of a concept converges on some
common meaning.
 Measured by correlating scores on subsets of items
making up a scale.

24. 13–24
Internal Consistency
• Split-half Method
 Assessing internal consistency by checking the
results of one-half of a set of scaled items against the
results from the other half.
• Coefficient alpha (α)
 The most commonly applied estimate of a multiple
item scale’s reliability.
 Represents the average of all possible split-half
reliabilities for a construct.

25. 13–25
Test-Retest Reliability
• Test-retest Method
 Administering the same scale or measure to the same
respondents at two separate points in time to test for
stability.
 Represents a measure’s repeatability.
• Problems:
 The pre-measure, or first measure, may sensitize the
respondents and subsequently influence the results of
the second measure.
 Time effects that produce changes in attitude or other
maturation of the subjects.

26. 13–26
Validity
• Validity
 The accuracy of a measure or the extent to which a
score truthfully represents a concept.
 Does a scale measure what was intended to be measured?
• Establishing Validity:
 Is there a consensus that the scale measures what it
is supposed to measure?
 Does the measure correlate with other measures of
the same concept?
 Does the behavior expected from the measure predict
actual observed behavior?

27. 13–27
Validity (cont’d)
• Face Validity
 A scale’s content logically appears to reflect what was intended
to be measured.
• Content Validity
 The degree that a measure covers the breadth of the domain of
interest.
• Criterion Validity
 The ability of a measure to correlate with other standard
measures of similar constructs or established criteria.
• Construct Validity
 Exists when a measure reliably measures and truthfully
represents a unique concept.

28. 13–28
Validity (cont’d)
• Convergent Validity
 Another way of expressing internal consistency;
highly reliable scales contain convergent validity.
• Discriminant Validity
 Represents how unique or distinct is a measure; a
scale should not correlate too highly with a measure
of a different construct.

29. 13–29
EXHIBIT 13.7 Reliability and Validity on Target

30. 13–30
Sensitivity
• Sensitivity
 A measurement instrument’s ability to accurately
measure variability in stimuli or responses.
 Generally increased by adding more response points
or adding scale items.