Transcript Ch 13 Introduction to Measurement

King Fahd University of Petroleum & Minerals
Department of Management and Marketing
MKT 345 Marketing Research
Dr. Alhassan G. Abdul-Muhmin
Introduction to Measurement
Reference: Zikmund, Chapter 13
Learning Objectives
At the end of this discussion you should be able to:
1. define measurement and explain its importance in
marketing research
2. list and explain the requirements for effective
measurement in marketing research
3. list and explain the different types of
measurement scales
4. know how to form an index or composite
measure
5. list and explain the criteria used to evaluate the
quality of index measures
6. Perform basic assessment of scale reliability and
validity
THE NATURE OF MEASUREMENT
1. The process of assigning numbers or scores to
attributes of people or objects.
2. The process of describing some property of a
phenomenon of interest by assigning numbers in a
reliable and valid way
Precise measurement requires:
a) Careful conceptual definition – i.e. careful definition of
the concept (e.g. loyalty) to be measured
b) Operational definition of the concept
c) Assignment rules by which numbers or scores are
assigned to different levels of the concept that an
individual (or object) possesses.
1. Conceptual Definition

Concept - A generalized idea about a class of
objects, attributes, occurrences, or processes.


Construct - A concept that is measured with
multiple variables.


Examples: Gender, Age, Education, brand
loyalty, satisfaction, attitude, market orientation
Examples: Brand loyalty, satisfaction, attitude,
market orientation, socio-economic status
Variable - Anything that varies or changes
from one instance to another; can exhibit
differences in value, usually in magnitude or
strength, or in direction.
1. Conceptual Definition


Concepts must be precisely defined for effective
measurement.
E.g. consider the following definitions of “brand
loyalty”:
1. “The degree to which a consumer consistently
purchases the same brand within a product
class.” (Peter & Olson)
2. “A favorable attitude toward, and consistent
purchases of, a particular brand”. (Wilkie, p.276)
 The two definitions have different implications for
measurement – they imply different
operationalizations of the concept of brand loyalty
2. Operational Definition/Operationalization

Operational definition - A definition that gives meaning to a
concept by specifying what the researcher must do (i.e. activities
or operations that should be performed) in order to measure the
concept under investigation.

Operationalization - The process of identifying scales that
correspond to variance in a concept.
For example:

Conceptual definition # 1 for brand loyalty in the previous slide
implies that in order to measure loyalty for brand A (operational
definition), you will need to:
1) Observe consumers’ brand purchases over a period of time, and
2) Compute the percent of purchases going to brand A

For conceptual definition # 2 you will need to:
1) Observe consumers’ brand purchases over a period of time,
2) Compute the percent of purchases going to brand A, and
3) Ask consumers questions to determine their attitudes toward
brand A
3. Rules of Measurement


Guidelines established by the researcher for assigning
numbers or scores to different levels of the concept (or
attribute) that different individuals (or objects) possess
The process is facilitated by the operational definition.

For example, if you operationalized brand loyalty as “purchase
sequences” (conceptual definition # 1), then you may establish the
following rules for assigning scores:
 If consumer purchased brand A:





90% or more –> loyalty for brand A = 1 (Extremely loyal)
80 - 89% –> loyalty for brand A = 2 (Very loyal)
70 - 79% –> loyalty for brand A = 3 (Loyal)
Etc.
In this case, we have assigned the numbers 1, 2, 3 to different
levels of loyalty toward brand A. We have measured loyalty
for brand A.
MEASUREMENT SCALES

To effectively carry out any measurement (whether
in the physical or social sciences) we need to use
some form of a scale.

A scale is any series of items (numbers) arranged
along a continuous spectrum of values for the purpose
of quantification (i.e. for the purpose of placing
objects based on how much of an attribute they
possess)

E.g. the thermometer consists of numbers arranged
in a continuous spectrum to indicate the magnitude
of “heat” possessed by an object.
Three Meanings of “Scale” in Marketing Research
There are three ways in which the word “scale” is used in
marketing research
1) The level at which a variable is measured (Level of scale
measurement)
–
–
2)
the arithmetical properties implied by the numbers assigned to levels
of an attribute possessed by an object (i.e. the unit of analysis)
Discussed in this chapter
An index, or composite measure of a construct
–
–
3)
Multiple statements used to measure a construct (also called a multiitem measure of the construct)
Discussed in this chapter
The response categories provided for a close-ended question
in a questionnaire, e.g.
–
–
Subjects expressed their agreement / disagreement on a 5-point
category scale or on a 5-point semantic differential scale.
Will be discussed in chapter 14
(1) LEVELS OF SCALE MEASUREMENT


Numbers assigned in measurement can take on different
levels of meaning depending on one of four mapping
characteristics possessed by the numbers:
1. Classification - The numbers are used only to group or
sort responses. No order exists
2. Order - The numbers are ordered. One number is greater
than, less than, or equal to another
3. Distance - Differences between the numbers are ordered.
The difference between any pair of numbers is greater
than, less than, or equal to the difference between any
other pair of numbers
4. Origin - The number series has a unique origin indicated
by the number zero
The type of mapping characteristic assumed depends on the
properties of the attribute (or construct) being measured
The Four Characteristics of Mapping Rules
1. Classification – The numbers are used only to
group or sort responses. No order exists
2. Order – The numbers are ordered. One
number is greater than, less than, or equal to
another
3. Distance – Differences between the numbers
are ordered. The difference between any pair
of numbers is greater than, less than, or equal
to the difference between any other pair of
numbers
4. Origin – The number series has a unique
origin indicated by the number zero
The Four Levels of Scale Measurement

1.
2.
3.
4.
Four levels of scale measurement result from this mapping
Nominal Scale: a scale in which the numbers or letters assigned
to an object serve only as labels for identification or
classification, e.g. Gender (Male=1, Female=2)
Ordinal Scale: a scale that arranges objects or alternatives
according to their magnitude in an ordered relationship, e.g.
Academic status (Sophomore=1, Freshman=2, Junior=3, etc
Interval Scale: a scale that both arranges objects according to
their magnitude, distinguishes this ordered arrangement in units
of equal intervals, but does not have a natural zero representing
absence of the given attribute, e.g. the temperature scale (40oC
is not twice as hot as 20oC)
Ratio Scale: a scale that has absolute rather than relative
quantities and an absolute (natural) zero where there is an
absence of a given attribute, e.g. income, age.
Nominal, Ordinal, Interval, and Ratio Scales Provide Different Information
Characteristics of Different Levels of Scale Measurement
Type of
Scale
Data
Characteristics
Numerical
Operation
Descriptive
Statistics
Nominal
Classification but no
order, distance, or
origin
Counting
Frequency in each
category
Percent in each
category
Mode
Gender (1=Male,
2=Female)
Ordinal
Classification and
order but no
distance or unique
origin
Rank ordering
Median
Range
Percentile ranking
Academic status
(1=Freshman,
2=Sophomore,
3=Junior,
4=Senior)
Interval
Classification, order, Arithmetic
and distance but no
operations that
unique origin
preserve order and
magnitude
Mean
Standard deviation
Variance
Temperature in
degrees
Satisfaction on
semantic
differential scale
Ratio
Classification, order, Arithmetic
distance and unique operations on
origin
actual quantities
Geometric mean
Coefficient of
variation
Age in years
Income in Saudi
riyals
Examples
Note: All statistics appropriate for lower-order scales (nominal being lowest) are appropriate for
higher-order scales (ratio being the highest)
(2) INDEX OR COMPOSITE MEASURES
•
•
Both index and composite measures use combinations (or collection) of
several variables to measure a single construct (or concept); they are
multi-item measures of constructs.
However, for index measures, the variables need not be strongly
correlated with each other, whilst for composite measures, the variables
are typically strongly correlated as they are all assumed to be measuring
the construct in the same way
Example 1: Index Measure
Construct: Social class
Measures: Linear combination (index) of occupation, education, income.
Social class = β1Education + β2Occupation + β2Occupation
Example 2: Composite Measure
Construct: Attitude Toward Brand A
Measures: Extent of agreement/disagreement with multiple statements:
a) “I like Brand A very much”
b) “Brand A is the best in the market”
c) “I always buy Brand A”
•
Statements a), b), c), constitute a “scale” to measure attitudes toward brand A
Computing Scale Values for Composite Scales
• 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.
CRITERIA FOR GOOD MEASUREMENT

Three criteria are commonly used to assess the
quality of measurement scales in marketing
research:
1. Reliability
2. Validity
3. Sensitivity
RELIABILITY


The degree to which a measure is free from
random error and therefore gives consistent
results.
An indicator of the measure’s internal
consistency
Test-Retest
Stability
(Repeatability)
Reliability
Internal
Consistency
Splitting
halves
Equivalent
forms
Assessing Stability (Repeatability)
•
Stability  the extent to which results
obtained with the measure can be reproduced.
1. Test-Retest Method
•
Administering the same scale or measure to the same
respondents at two separate points in time to test for
stability.
2. Test-Retest Reliability 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.
Assessing Internal Consistency
•
Internal Consistency: the degree of homogeneity
among the items in a scale or measure
1. Split-half Method
•
Assessing internal consistency by checking the results of onehalf 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.
2. Equivalent forms
•
Assessing internal consistency by using two scales designed to
be as equivalent as possible.
VALIDITY
• The accuracy of a measure or the extent to
which a score truthfully represents a concept.
• The ability of a measure (scale) to measure what
it is intended measure.
• Establishing validity involves answers to the ff:
– 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?
Validity
Face or
Content
Criterion
Validity
Concurrent
Construct
Validity
Predictive
ASSESSING VALIDITY
1. Face or content validity: The subjective agreement
among professionals that a scale logically appears to
measure what it is intended to measure.
2. Criterion Validity: the degree of correlation of a
measure with other standard measures of the same
construct.
•
•
Concurrent Validity: the new measure/scale is taken at
same time as criterion measure.
Predictive Validity: new measure is able to predict a future
event / measure (the criterion measure).
3. Construct Validity: degree to which a measure/scale
confirms a network of related hypotheses generated
from theory based on the concepts.
•
•
Convergent Validity.
Discriminant Validity.
Relationship Between Reliability & Validity
1. A measure that is not reliable cannot be
valid, i.e. for a measure to be valid, it must
be reliable  Thus, reliability is a necessary
condition for validity
2. A measure that is reliable is not necessarily
valid; indeed a measure can be but not valid
 Thus, reliability is not a sufficient
condition for validity
3. Therefore, reliability is a necessary but not
sufficient condition for Validity.
Reliability and Validity on Target
SENSITIVITY
• The ability of a measure/scale to accurately measure
variability in stimuli or responses;
• The ability of a measure/scale to make fine distinctions
among respondents with/objects with different levels of
the attribute (construct).
– Example - A typical bathroom scale is not sensitive enough to be used to
measure the weight of jewelry; it cannot make fine distinctions among
objects with very small weights.
• Composite measures allow for a greater range of
possible scores, they are more sensitive than single-item
scales.
• Sensitivity is generally increased by adding more
response points or adding scale items.