Transcript Measurement Theory in Marketing Research

Measurement Theory in
Marketing Research
Measurement
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What is measurement?
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Assignment of numerals to objects to represent
quantities of attributes
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Don’t measure the object -- measure attributes of the
object
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Don’t measure a person -- measure their weight, height,
social class, GPA, etc.
Definition does not suggest how to measure the
attributes
Measurement
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Measurement Scales
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Four types -- NOIR
Nominal -- Number is used for identification
purposes
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Jon Laczniak is number 5
Matt Laczniak is number 9
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Numbers reflect nothing -- just used to identify the person
Measurement
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Ordinal -- Number is used to reflect order
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Jon Laczniak is in 7th grade at Ames Middle School; Ethan
Constant is in 3rd Grade
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Jon is in a “higher” grade
How much higher?
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Cannot really tell (depends on programs, etc.)
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No true 0 and differences between grades is not constant
Interval -- Number reflects “intervals” between attributes
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Matt Laczniak scored 96 on his soccer skills test;
Jon Laczniak scored a 32
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Matt scored 64 points higher than Jon!
Is he three times as good?
Can we really say that someone has “0” soccer skills?
Measurement
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Ratio -- Number has an absolute 0
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Andy Laczniak is 22 years old; Jon Laczniak is 11
years old
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Andy is twice as old as Jon
Age has a real (and interpretable) 0
Measurement
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XO = XT + (ES + ER)
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XO= Observed score for some construct
XT = True score for some construct
ES = Systematic Error
ER = Random Error
Measurement
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Objective in research -- XO = XT
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When this happens, the measure is valid
If XR= 0; XO = XT + ES
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Measure is reliable
Free of random error
Measurement
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Reliability -- instrument measures the same
concept every time it is used (XO = XT + ES)
Validity -- instrument measures what it
intends to measure (XO = XT)
Given that XO = XT + ES suggests XO is free
of random error -- this indicates reliability
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Reliability is a necessary, but not sufficient
indicator of validity
Measurement
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Assessing Reliability (XO = XT + ES )
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Consistent responses across time (test/retest reliability)
Internally consistent -- all aspects of the measure work
together
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Multiple measures (of the same concept) are needed
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Laczniak Yogurt is:
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Good/Bad; Favorable/Unfavorable; Positive/Negative
Coefficient alpha = k (mean inter-item correlation)/{1 +[ (k-1)
(mean inter-item correlation)]}
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Here – 3 items are used to measure attitude
Need to calculate correlations between each item (3) and then
compute the mean
Measurement
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Calculation of coefficient alpha (a)
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k (mean inter-item correlation)/1 +[ (k-1) (mean inter-item
correlation)]
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Thus, if one item is used
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Mean correlation = 0
Thus, a = 0
Single item measures have reliability = 0
Example
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Where k = number of items used to measure a concept
K=3
Mean inter-item correlation = .80
a = ??
Rules of Thumb
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a = .70 (for new/exploratory measures of concepts)
a = .85 (for measures that have previously been shown to be reliable)
Measurement
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Indicators of Validity (XO = XT)
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Face validity
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Discriminant Validity
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Measure “looks” like it should
Best to have others (“expert judges” determine this)
Measure does not measure some other concept
Correlation with measure of other concept is very low
Convergent Validity
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Measure corresponds to other measures of this concept
Correlation with other measures of this concept is high
An Example of Measurement
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Ranking versus Rating
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Rank -- respondent orders the brands according
to their attitude (Ordinal Scale)
Rating -- respondent rates each brand on a similar
scale (Interval Scale) ***
An Example of Measurement
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Likert Scales -- scales in which respondents
indicate their degree of (dis) agreement with
statements about the object
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Generate large number of statements about the
attitude object (e.g, “Professor Laczniak is an
exceptional instructor”)
Classify the statements a priori as (un) favorable
An Example of Measurement
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Likert Scales (cont’d)
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Determine a method of scoring (3-point versus 5-point
versus 7-point or more; use a midpoint or not – 4-point)
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Purify the scale by eliminating ambiguous items (through
pre-tests)
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Agree, Neutral, Disagree
Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree
If an item makes the alpha coefficient lower – drop it
Use the scale
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Has a midpoint (can say if mean response is above/below it)
Is interval scaled -- can make mean comparisons
Must develop your own norms (midpoints do not always apply)
An Example of Measurement
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Semantic Differential -- uses a series of 7-point
gradations with bipolar adjectives that anchor the
beginning and end of each scale
Most commonly used -- “My attitude toward the
Compaq brand is:”
Good ___:___:___:___:___:___:___ Bad
Positive ___:___:___:___:___:___:___ Negative
Favorable ___:___:___:___:___:___:___ Unfavorable
Measurement
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Internal versus External Validity
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Internal Validity – ability to demonstrate that the
an observed effect is due to the experimental
manipulation (Lab Setting)
External Validity – ability to generalize the results
of an experiment beyond the experimental
subjects (Real World)
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Internally valid studies are typically not “real”
Externally valid studies typically have less controls
Ideally, we follow a lab study with one in the real
world