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demographic and financial variables for respondents. Among the variables recorded
is age, which is organized into several mutually exclusive age categories (18–25,
26–34, 35–46, and 47 and older). Respondents are also queried for a response or
opinion, good or bad , about some consumer product. Cross-tabulation permits the
analyst to determine the number of respondents in the 35–46 age category that report
the product to be good . The analysis can also determine the number of respondents
that fit both our conditions (age and response) as a percentage of the total.
The PivotTable and PivotChart report functions are found in the Insert Ribbon .
Both reports are identical, except that the table provides numerical data in table
form, while the chart converts the numerical data into a graphical format. The best
way to proceed with a discussion of the cross-tabulation capabilities of PivotTable
and PivotChart is to begin with an illustrative problem, one that will allow us to
exercise all the capabilities of these powerful functions.
5.3.1 An Example
Now let us consider an example, a consumer survey, to demonstrate the uses
of PivotTable s and PivotChart s. The data of interest for the example is shown
in Table 5.2. A web-based business, TiendaMí, 2 is interested in testing
various web designs that customers will use to order products. The owners of
TiendaMí hire a marketing firm to help them conduct a preliminary survey
of 30 randomly selected customers to determine their preferences. Each of the cus-
tomers is given a gift coupon to participate in the survey and is instructed to visit
a website for a measured amount of time. The customers are then introduced to
four web-page designs and asked to respond to a series of questions. The data are
self-reported by the customers on the website as they experience the four differ-
ent webpage designs. The marketing firm has attempted to control each step of the
survey to eliminate extraneous influences on the respondents. Although this is an
example, it is relatively typical of consumer opinion surveys and website tests.
In Table 5.2, the data collected from 30 respondents regarding questions about
their gender, age, income, and the region of the country where they live are orga-
nized as before. Each respondent, often referred to as a case, has his data recorded in
a row. Respondents have provided an Opinion on each of the 4 products in one sec-
tion of the data, and demographic characteristics, Category , in another. As is often
the case with data, there may be some data elements that are either out of range or
simply ridiculous responses; for example, respondent number 13 in Table 5.2 claims
to be a 19 year old female that has an income of $55,000,000 and resides in outer
space. This is one of the pitfalls of survey data: it is not unusual to receive informa-
tion that is unreliable. In this case, it is relatively easy to see that our respondent is
not providing information that we can accept as true. My position, and that of most
TiendaMía in Spanish translates to My Store in English
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