Questionnaire Scales Part 2

Questionnaire Scales: Part 2
Slide 1
Part two of this lecture on questionnaire scales continues with a discussion of non-comparative
and comparative scales.
Slide 2
If you’ve made it through Part 1 of this lecture, then you know that I urge you to use Likert
scales because they’re the easiest to construct, the least confusing to respondents, and
functionally equivalent to any other scale format you might select. What are the formatting
issues for designing non-comparative scales in general, but Likert scales in particular? There
are five issues.
You should provide verbal descriptions for each category, and those descriptions must
be very concise and precise.
You’ll need to choose the number of categories for the Likert-type items. To discriminate
between people—and in marketing, discrimination is a good word because it means
trying to differentiate groups of people according to their preferences—may mean
spotting subtle differences. If you write questions with few response choices, then it’ll be
difficult to identify distinct groups of people. The rule of thumb is that scale items should
have at least four categories, but typically five to nine categories. If you provide more
than nine categories, people will be unable to make clear distinctions, like the difference
between ‘15’ and ‘16’ on a 20-point scale.
You’ll need to choose either a balanced or unbalanced scale. By balanced, I mean an
equal number of positive and negative scale points; unbalanced means an unequal
number of those points. Conventional wisdom dictates that you use balanced scales
unless you know that respondents tend to respond toward one or the other end of the
scale. This unbalanced problem is an issue for ethics research; due to social desirability
bias, many respondents tend to answer toward the positive end of the scale. Spreading
the positive end of the scale makes it easier to differentiate among the people crowding
the positive end of the scale.
You’ll need to decide whether or not to use an odd or even number of categories or
scale points. This is a somewhat arbitrary decision. I recommend that you use an odd
number of scale points only if respondents could be truly neutral or indifferent to a Likerttype statement. By using an even number of scale points, you force someone to fall on
one or the other side of the fence. If you provide an odd number with middle, neutral
point, respondents can become lazier and respond ‘neutral’ instead of carefully
considering whether they are slightly more favorable or unfavorable to that statement.
Finally, you’ll need to decide whether or not you force respondents to answer your
question. By force, I mean excluding a don’t know answer option. Without a don’t know
option, people who have no other opinion often circle the midpoint of the scale, hence
confusing lack of knowledge with indifference. If you believe respondents could be
unknowledgeable about the statement, then you should allow for a don’t know response.
I’ll show examples of all five issues on the next slide.
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Slide 3
In this first example about the taste of Wonder Bread, the five scale items are balanced. It’s a
forced choice because you’re not giving respondents the option of saying they don’t know and
there’s an odd number of scale points. This scale assumes the possibility of an indifference
point for someone’s attitude toward the taste of Wonder Bread.
In the second example about Ultra Bright Toothpaste, the scale is balanced; the number of
positive and negative statements is identical. It’s also forced choice, but now there’s an even
number of intervals. This scale assumes that respondents can have either a somewhat positive
or somewhat negative opinion, but could not be indifferent about Ultra Bright Toothpaste.
In the third example about the reaction to an ad, it’s an unbalanced scale because there are
three favorable statements and only one negative statement. It’s a forced choice item because
respondents don’t have the option of answering don’t know, and there’s an odd number of
Finally, in the last example about a Sears downtown store, the item is balanced in the sense
that there are as many positive as negative statements. It’s not forced response because there’s
an ‘I don’t know’ option, and there’s an odd number of intervals excluding the ‘don’t know’
response, which is off the scale continuum. All of these formats are perfectly reasonable.
Slide 4
As I mentioned in an earlier lecture, often we develop multiple items to assess objects on a
given attribute. This slide summarizes the approach for developing a multi-item scale to assess
a single construct like store image. First, I’d review the theoretical work on store image. Based
on that work, I’d generate a large pool of items suggested by theory, secondary data, and any
qualitative research. Next, I’d select a reduced set of items based on expert judges. I might, for
example, develop a set of 40 items and then ask several colleagues to examine those items and
select the ones they believe best represent the construct. Then, I’d take that reduced set of
items, administer them to a sample of respondents, analyze their responses, and ultimately
create a reduced set of items that would constitute my final scale. The technical aspects of the
requisite statistical analysis will be addressed in the subsequent lecture.
Slide 5
Returning to single-item scales, here’s an example of one you might find in many marketing
research questionnaires: a purchase-intention scale. The top scale contains five points and the
bottom scale contains 11 points.
Slide 6
Here are alternative formats for purchase-intent questions that relate to my comments about
question formatting. In the first example, the scale is balanced and has a neutral point. In the
second example, it’s balanced without a neutral point. In the third example, it’s balanced but it’s
not forced because there’s a don’t know answer choice. (I’m dubious about placing that choice
as the third choice on the scale because that placement implies it’s part of the continuum, as
opposed to an option.) The fourth example is a graphic scale; the fifth example is dichotomous,
in the sense that there are but two choices; would or would not buy; and in the last example, the
purchase-intent scale is unbalanced because there are more items related to possibility of
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Slide 7
Graphic rating scales present respondents with a graphic continuum and ask them to respond
accordingly. There are several circumstances under which graphic rating scales might be
useful; in particular, when respondents’ language capabilities are suspect.
Slide 8
Language isn’t an issue for this ladder scale, but it serves as an analogy for the way people
think about life and climbing a ladder to success. The top of the ladder represents the best
possible life and the lowest rung in the ladder represents the worst possible life. In a way, this
graphic symbolizes the underlying construct.
Slide 9
Here’s an example of a thermometer scale, which is used to evaluate the quality of food at a
restaurant called Outpost’s Steak n’ Fries. I’m uncertain why researchers would use such a
scale, other than its novelty inducing a higher response rate.
Slide 10
The next three slides present scales that younger children might use to indicate their attitudes
toward an object. Younger children’s verbal abilities may be minimal; as a result, using these
types of scales may provide more accurate assessments of their attitudes. For example, I
assume that respondents to the first scale, which asks “How much did you like the boy in the
commercial?” are meant to circle one of these three pictures; smile, neutral, or frown. This reply
should be indicative of that respondent’s assessment of the boy in the commercial.
Slide 11
Here is a smiling-face scale. Although the verbal instructions are present, the child doesn’t read
them; instead, an interviewer reads these instructions. “Tell me how much you like the Pull-back
teddy bear by pointing to the face that best shows how much you like it. If you did not like the
Pull-back teddy bear at all, you should point to Face 1. If you liked it very much, you should
point to Face 4. Now, how much did you like the Pull-back teddy bear?” Young children could
respond to a question with this format.
Slide 12
Graphic scales could be used for children or for adults with language limitations. The number of
stars, where five stars is really liked and one star is really hated, or the stick figures, where the
one with open arms means ‘liked it a lot’ and the one with the thumb pointing downward means
‘didn’t like it all’. It’s very similar to the slide #10, with the child liking the boy in the commercial.
Slide 13
Ignoring constant sum scales for the moment, this table provides a good summary of the
relative advantages and disadvantages for the different scales I’ve discussed to this point.
Slide 14
As a quick reminder, non-comparative scales ask respondents to consider one attribute or one
object at a time, whereas comparative scales ask respondents to consider multiple attributes or
multiple objects at one time.
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Slide 15
Ranking scales are a type of comparative scale. Here’s an example of a ranking order scale for
eye shadow. There are six different brands being ranked on three different characteristics: the
quality of the container, the quality of the applicator, and the quality of the eye shadow itself.
This is as complex a rating task as I’d recommend you ask respondents to perform. Ranking
more than a half-dozen things on a given attribute is probably too difficult for most respondents.
Although such scales are reliable indicators of the most preferred (or highest ranked) and the
least preferred (or lowest ranked), the rankings for all other objects are unreliable.
Slide 16
Here’s the type of data we might collect for rank ordering of four items. In this case, 10 people
have been asked to rank order four items: a, b, c, and d. Person #1 ranked ‘B’ most preferred,
‘A’ second-most preferred, ‘C’ third-most preferred, and ‘D’ least preferred. Similarly, Persons
#2 through #10 ranked the same four items.
Slide 17
As I mentioned in the lecture on levels of measurement, researchers must analyze rank-order
data carefully; it’s not intervally or ratio scaled, and it’s not parametric data. As a result, such
data cannot be analyzed with traditional statistics. Researchers can’t take the mean of the ranks
and say object ‘A’ has the highest mean rank. Instead, they must create tabulations like the one
depicted on this slide. There are four brands: A, B, C, and D. This table summarizes the
previous data table by presenting the number of times each brand is ranked first, second, third,
and fourth. This table shows a meaningful and statistically correct way to summarize the data on
the previous slide.
Slide 18
Paired-comparison scales have certain favorable psychometric properties relative to ranking
scales. Respondents are presented with two objects at a time and asked to pick the one they
prefer. This is a relatively psychometrically simple task, so almost all respondents can perform it
properly. Think about when you’ve been asked to compare audio speakers. After the
salesperson asked you about your budget and the type of music you like, he or she ushered you
into a listening room, picked two different pairs of speakers, and then played music of the type
you like, first on one set of speakers and then another set, and then back to first set, and then
back to the second set, et cetera. Going back and forth allows you to compare effortlessly; even
people with uneducated ears can hear differences if asked to compare speaker system #1 to
speaker system #2. However, if that same salesperson asked you to compare five different
speaker systems at one time, you’d be hard-pressed to do so well. By the time you’d be
listening to speaker system #3, you’d no longer recall how speaker system #1 sounded. People
can easily respond about two things at a time; it’s beyond most people’s ability to respond
meaningfully about four or five things at a time.
Paired-comparison scales create more reliable rank-ordering data with one proviso, which is
specified in the second bullet point: the large number of scales often needed to rank things from
most to least preferred or most to least important. Assume 10 brands that respondents must
rate from most to least preferred. For a ranking scale, the 10 items would be listed and people
asked to put a number 1 through 10 next to each item in accord with how they rank it from most
to least preferred. Although a seemingly straightforward task, people won’t do it well because
they’re being asked to compare too many things at the same time. This ranking question could
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be asked as a set of paired-comparison scales: Which do you prefer, 1 or 2? 1 or 3? 1 or 4? et
cetera. To complete this task, people would respond, as the formula indicates, to 10 x 9 = 45
separate questions. Instead of filling in ten numbers, they would need to respond to 45 separate
questions, which can be fatiguing. Once respondents become fatigued, they’ll no longer
carefully discriminate between the objects, and their answers will become unreliable, thus
defeating the purpose of using paired-comparison scales instead of rank-ordering scales. I
recommend that you never ask people to rank more than a half-dozen things at a time. If you
want them to rank up to 10 or 11 things, consider paired-comparison scales. If you want them to
rank more than 10 or 11 things, there are alternatives I’ll discuss in a subsequent lecture.
Slide 19
Here’s an example of using paired-comparison scales to plan an ad for a restaurant.
Restaurants have different features, such as type of food, fun place to go, prices, location,
service, and atmosphere. If people rank those six things from most to least important, the things
ranked most and least important will be ranked reliably, but not the other four things. In
designing an ad, the restaurant owner would like to know the most important attributes people
think about when selecting a restaurant. As opposed to asking them to rank those six things, the
researcher could provide respondents with this paired comparison-table and ask them which is
more important: type of food or service; fun place to go or quality of food. This pairedcomparison task is relatively simple from a psychometric standpoint.
Slide 20
Here’s another example of an abbreviated paired-comparison set for suntan products. I include
this slide to show that the instructions for these types of questions are relatively simple. It’s likely
respondents will read such instructions and respond accordingly.
Slide 21 (No Audio)
Slide 22
Another type of comparative scale is the constant-sum scale. As I mentioned in the lecture on
levels of measurement, constant-sum scales have one very favorable property: they generate
ratio-scale data. In this example, respondents are asked to allocate 100 points across seven
characteristics of tennis sportswear. If comfortable to wear received 20 points, and made in the
USA received 10 points, it’s safe to say that comfortable to wear is twice as important as made
in the USA. That’s a level of analyses that is unavailable when dealing with nominal or interval
scaled data.
One limitation of constant-sum scales is that most respondents will be unfamiliar with them, may
not read the instructions properly, and as a result, they may just look under the column number
points and check those features they believe are most important. Such data are unusable for
subsequent analysis.
Slide 23
Here’s an example of a constant-sum scale for automobiles. Although the points do sum
properly, it’s easy to norm them; for example, if someone inadvertently allocates only 80 points,
then multiplying all the points by 5/4ths creates a sum of 100 points. Such norming allows that
person’s responses to be added to other people’s responses in a meaningful way. Administering
a constant-sum scale via the Internet causes this problem to vanish because the software can
be programmed to norm the data and force it to sum to 100 points.
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Slide 24
Here’s an example of a poor constant-sum scale that I found in a marketing research textbook I
used several years ago. It asks respondents to allocate 10 points in accord with the last 10
times they purchased shampoo. This scale is limited in two ways. First, people will not recall
what shampoo they bought the ninth or tenth time ago. Given the frequency with which people
buy shampoo—perhaps once every three to four months—you’re asking them to recall
shampoo they purchased three years ago. The likelihood of remembering that correctly is very
low; hence, the time horizon for this question is problematic. Second, the 10 points must be
allocated across far too many items. If you wonder why I sometimes show you poorly formatted
scales, it’s because they’re the best tool for explaining what you should avoid.
Slide 25
Here’s an example of a weighted-paired-comparison scale; it’s called constant sum with paired
comparison. The instructions ask respondents to divide 11 points between each pair of hand
and body lotions. Points are divided in such a way that the more preferred thing receives more
points than the less preferred thing in proportion to the degree of preference. Nothing can
receive more than 11 points. Such data reveals what is preferred (A or B) and the degree to
which it’s preferred.
Slide 26
Q sorts are a method for sorting a large number of things, as will be illustrated on the next two
Slide 27
Suppose we’re interested in having people sort or rank 75 magazines from most to least
preferred. Such a task is impossible for a paired-comparison approach, and the data we’ll
receive from a traditional ranking approach would be highly unreliable. How best to identify the
most and least preferred magazines out of a set of 75 magazines? One possibility is to give
people a mechanical sorting task. In this case, people receive a deck of cards, and on each
card is a picture of a magazine. The instructions read “Please choose nine magazines you most
prefer of the 75. Once you’ve selected the nine most preferred, please list the magazine name
on the form in the column headed Most Preferred. Now select the next nine.” This is one way to
sort the magazine. Another way to run a Q sort is to provide the pile of 75 cards and ask people
to divide the cards into two piles: the more preferred versus less preferred pile, which essentially
asks for repeated paired comparisons. Here’s a magazine, put it in one of two categories. After
they’ve sorted the 75 cards into two piles of most and least preferred, ask them to take the pile
of most preferred pile and divide it into two piles: the most preferred of the more preferred and
the less preferred of the more preferred. By mechanically sorting cards in this fashion, people
are making repeated dual comparisons; in other words, breaking down the ranking of 75 items
into a series of paired comparisons. This format makes this task doable for respondents.
Slide 28
Here’s another example of a Q sorting task. In this case, respondents are asked to sort 100
bank advertising slogans from most unique to least unique. The advertiser assumes that the
more unique slogans will be more memorable and hence more effective, and the least unique
slogans will be less memorable and hence less effective. Both this example and the previous
example require manual sorting of physical cards. Q sorts also can be performed online with
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computer software. There isn’t a requirement that respondents have access to physical cards
for the sorting process; the sorting process also could be done in a virtual space.
Slide 29 (No Audio)
Slide 30
The dollar-metric scale is a favorite scale (of mine) because it yields high quality data and it’s
easy for respondents to use. Here, the question relates to different types of containers for fruit
juice. Respondents are asked to indicate which of two different forms they most prefer and then
how much more they’d be willing to pay for juice delivered in that type of container (relative to
the unchosen container). This type of information can help to make sound design decisions. In
this example, the glass container is preferred to the can container by $.07. If a juice producer
decided to introduce glass containers and only charge an additional $.05 for those containers
(relative to the juice sold in cans), then customers would be likely to buy that glass-enclosed
juice because they’ve received a bargain. They’re willing to pay $.07 more for juice in a glass
container but are being asked to pay only $.05 more. Dollar-metric data can be used with cost
data to help marketers optimize the design of their products.
Slide 31
Magnitude-estimation scales are similar to constant sum scales plus paired-comparison scales.
In this case, people are asked, on a scale of 0 to 100, to indicate the relative degree to which
they agree or disagree with a certain statement.
Slide 32
If we consider the endpoints of each line as two different notions, then line-marking scales are
another example of comparative scales. In the case of this marking scale, the proximity of the X
to each endpoint indicates the degree to which respondents believe that endpoint describes the
Slide 33
Here’s a summary of the relative advantages and disadvantages of various comparative scales.
Slide 34 (No Audio)
Slide 35
To briefly recap this lecture on questionnaire scales, I describe the various non-comparative and
comparative scales that can be used in a questionnaire. For non-comparative scales, I gave
many reasons for preferring Likert-type scales. For comparative scales, I recommended that
you always make the respondent’s task reasonable. Rank-order scales are acceptable for
ranking a few items. Paired-comparison scales are preferred for ranking more than a few items.
If many items must be ranked, then a Q sort is required.
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