Microsoft Office Tutorials and References
In Depth Information
Table 2.1 Data categorization
Data
Description
Properties
Examples
Nominal or
Categorical
Data
Data that can be
placed into
mutually exclusive
categories
Quantitative
relationships among
and between data
are meaningless and
descriptive statistics
are meaningless
Country in which you
were born, a
geographic region,
your gender—these
are either/or
categories
Ordinal Data
Data are ordered or
often ranked
according to some
characteristic
Categories can be
compared to one
another, but the
difference in
categories is
generally
meaningless and
calculating averages
is suspect
Ranking breakfast
cereals—preferring
cereal X more than
Y implies nothing
about how much
more you like one
versus the other
Interval Data
Data characterized and
ordered by a
specific distance
between each
observation, but
having no natural
zero
Ratios are
meaningless, thus
15 degrees Celsius
is not half as warm
as 30 degrees
Celsius
The Fahrenheit (or
Celsius)
temperature scale or
consumer survey
scales that are
specified to be
interval scales
Ratio data
Data that have a
natural zero
These data have both
ratios and
differences that are
meaningful
Sales revenue, time to
perform a task,
length, or weight
ounces of coffee is, in fact, twice as heavy as 8 ounces. Ultimately, the ratio scale
has the highest information content of any of the measurement scales.
Just as thorough problem definition is essential to problem solving, careful selec-
tion of appropriate data categories is essential in a data collection effort. Data
collection is an arduous and often costly task, so why not carefully plan for the
use of the data prior to its collection? Additionally, remember that there are few
things that will anger a cost conscious superior more than the news that you have to
repeat a data collection effort.
2.3 Data Context and Data Orientation
The data that we collect and assemble for presentation purposes exists in a particular
data context : a set of conditions or an environment related to the data. This context
is important to our understanding of the data. We relate data to time (e.g. daily,
quarterly, yearly, etc.), to categorical treatment (e.g. an economic downturn, sales in
Europe, etc.), and to events (e.g. sales promotions, demographic changes, etc.). Just
as we record the values of quantitative data, we also record the context of data—
e.g. revenue generated by product A, in quarter B, due to salesperson C, in sales
 
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