Measurement Theory in Marketing Research

Measurement

What is measurement? Assignment of numerals to objects to represent

quantities of attributes Don’t measure the object -- measure attributes of the

object Don’t measure a person -- measure their weight, height,

social class, GPA, etc. Definition does not suggest how to measure the

attributes

Measurement

Measurement Scales Four types -- NOIR Nominal -- Number is used for identification

purposes Jon Laczniak is number 5 Matt Laczniak is number 9

Numbers reflect nothing -- just used to identify the person

Measurement

Ordinal -- Number is used to reflect order Jon Laczniak is in 7th grade at Ames Middle School; Ethan

Constant is in 3rd Grade Jon is in a “higher” grade How much higher?

Cannot really tell (depends on programs, etc.) No true 0 and differences between grades is not constant

Interval -- Number reflects “intervals” between attributes

Matt Laczniak scored 96 on his soccer skills test; Jon Laczniak scored a 32 Matt scored 64 points higher than Jon! Is he three times as good? Can we really say that someone has “0” soccer skills?

Measurement

Ratio -- Number has an absolute 0 Andy Laczniak is 22 years old; Jon Laczniak is 11

years old Andy is twice as old as Jon Age has a real (and interpretable) 0

Measurement

XO = XT + (ES + ER) XO= Observed score for some construct

XT = True score for some construct

ES = Systematic Error

ER = Random Error

Measurement

Objective in research -- XO = XT When this happens, the measure is valid

If XR= 0; XO = XT + ES Measure is reliable Free of random error

Measurement

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 Reliability is a necessary, but not sufficient

indicator of validity

Measurement

Assessing Reliability (XO = XT + ES ) Consistent responses across time (test/retest reliability) Internally consistent -- all aspects of the measure work

together Multiple measures (of the same concept) are needed

Laczniak Yogurt is: Good/Bad; Favorable/Unfavorable; Positive/Negative

Coefficient alpha = k (mean inter-item correlation)/{1 +[ (k-1) (mean inter-item correlation)]} Here – 3 items are used to measure attitude Need to calculate correlations between each item (3) and then

compute the mean

Measurement

Calculation of coefficient alpha () k (mean inter-item correlation)/1 +[ (k-1) (mean inter-item correlation)]

Where k = number of items used to measure a concept Thus, if one item is used

Mean correlation = 0 Thus,= 0 Single item measures have reliability = 0

Example K = 3 Mean inter-item correlation = .80 = ??

Rules of Thumb = .70 (for new/exploratory measures of concepts) = .85 (for measures that have previously been shown to be reliable)

Measurement

Indicators of Validity (XO = XT) Face validity

Measure “looks” like it should Best to have others (“expert judges” determine this)

Discriminant Validity Measure does not measure some other concept Correlation with measure of other concept is very low

Convergent Validity Measure corresponds to other measures of this concept Correlation with other measures of this concept is high

An Example of Measurement

Ranking versus Rating 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

Likert Scales -- scales in which respondents indicate their degree of (dis) agreement with statements about the object 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

Likert Scales (cont’d) Determine a method of scoring (3-point versus 5-point

versus 7-point or more; use a midpoint or not – 4-point) Agree, Neutral, Disagree Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree

Purify the scale by eliminating ambiguous items (through pre-tests) If an item makes the alpha coefficient lower – drop it

Use the scale 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

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

Internal versus External Validity 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) 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