2. • Measurement means assigning numbers or
symbols to characteristics of objects according
to certain pre-specified rules.
– One-to-one correspondence between the
numbers and the characteristics being
measured.
– The rules for assigning numbers should be
standardized and applied uniformly.
– Rules must not change over objects or time.
3. • Scaling involves creating a continuum upon which
measured objects are located.
• E.g. Consider an attitude scale from 1 to 100. Each
respondent is assigned a number from 1 to 100, with 1
= Extremely Unfavorable, and 100 = Extremely
Favorable.
• Measurement is the actual assignment of a number from
1 to 100 to each respondent.
• Scaling is the process of placing the respondents on a
continuum with respect to their attitude toward
department stores.
4. • LEVEL OF MEASUREMENT
• Level of Measurement is the relationship of the values
that are assigned to the attributes for a variable (e.g.
variable – gender, and attribute – female & male)
• The types of primary scales/levels of measurement
– Nominal
– Ordinal
– Interval
– Ratio
5. • The levels of measurement/ Kinds of Data: can also
• Qualitative / Discrete Data
– Separate, indivisible categories (e.g. male, female)
• Nominal (categorical)
• Ordinal
• Quantitative / Continuous Data
– Infinite number of possible values that fall between
two observed values
• Interval
• Ratio
6. • Nominal Measurement :
• A nominal scale is one that allows the researcher to
assign subjects to certain categories or groups.
• The attribute of the value names the variable uniquely,
e.g. the variable of gender has the unique attributes of
male and female.
• There is no ordering of the attributes
• The information that can be generated from nominal
scaling is to calculate the percentage (or frequency) of
males and females in our sample of respondents.
7. • Ordinal Measurement
• when attributes can be rank-ordered or distances
between attributes do not have any meaning
• for example, code Educational Attainment as 0=less
than H.S.; 1=some H.S.; 2=H.S. degree; 3=some
college; 4=college degree; 5=post college
• is distance from 0 to 1 same as 3 to 4?
8. • Interval Measurement:
• when distance between attributes has meaning
• for example, temperature (in Fahrenheit) - distance
from 30-40 is same as distance from 70-80, but note
that ratios don’t make any sense
• 80 degrees is not twice as hot as 40 degrees (although
the attribute values)
9. • Ratio Measurement: has an absolute zero that is
meaningful
• Can construct a meaningful ratio (fraction)
• For example, number of clients in past six months it is
meaningful to say that “...we had twice as many clients
in this period as we did in the previous six months”
• Ratio scale: overcomes the disadvantage of the
arbitrary origin point of the interval scale, in that it has
an absolute (in contrast to an arbitrary) zero point,
which is a meaningful measurement point
• The ratio scale is the most powerful of the four scales
because it has a unique zero origin ( not an arbitrary
origin).
11. The Hierarchy of Levels
Nominal
Interval
Ratio
Attributes are only named; weakest
Attributes can be ordered
Distance is meaningful
Absolute zero
Ordinal
12. • 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
14. 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.
15. 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 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.
2. Equivalent forms: Assessing internal consistency
by using two scales designed to be as equivalent as
possible.
16. 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?
18. 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).
19. 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.
20. 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.
22. 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.
23. • 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.
24. • Measurement Errors:
• Virtually all measurements have errors
• Reliability and Measurement Error are not the same,
rather Reliability infers an acceptable degree of
Measurement Error.
• Types of Errors:
• Determinate (or Systematic)
– Sometimes called bias due to error in one
direction- high or low
– Known cause
• Operator
• Calibration of glassware, sensor, or instrument
25. – When determined can be corrected
– May be of a constant or proportional nature
• Indeterminate Random Error (sampling error)
• Random error is caused by any factors that randomly
affect measurement of the variable across the sample
• The important thing about random error is that it does
not have any consistent effects across the entire sample.
• The important property of random error is that it adds
variability to the data but does not affect average
performance for the group, thus, random error is
considered noise.
26. • How is precision enhanced or improved?
• Increase sample size
– balance value of greater precision in study results
with costs in increasing number of subjects
• Standardize measurement methods
– e.g. questionnaire design, interviewer training
• Repeating and averaging measurements
• A precise measurement is one that has nearly the
same value on repeated measure usually over short
time interval (Namely where experiential change does
not affect measurement value)
• .
