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and
interval
), whether it is in dollars or some other quantitative measure, to inform

and inﬂuence an audience. In Chaps. 4 and 5 we will acknowledge that not all data

are numerical by focusing on qualitative (
categorical/nominal
or
ordinal
) data.

The process of data gathering often produces a combination of data types, and

throughout our discussions it will be impossible to ignore this fact: quantitative and

qualitative data often occur together.

Unfortunately, the scope of this topic does not permit in depth coverage of

the data collection process, so I strongly suggest you consult a reference on data

research methods before you begin a signiﬁcant data collection project. I will make

some brief remarks about the planning and collection of data, but we will gener-

ally assume that data has been collected in an efﬁcient and effective manner. Now,

let us consider the essential ingredients of good data presentation and the issues

that can make it either easy or difﬁcult to succeed. We will begin with a general

discussion of data: how to classify it and the context or orientation within which

it exists.

2.2 Data Classiﬁcation

Skilled data analysts spend a great deal of time and effort in planning a data collec-

tion effort. They begin by considering the type of data they
can
and
will
collect in

light of their goals for the use of the data. Just as carpenters are careful in selecting

their tools, so are analysts in their choice of data. You cannot ask a
low precision

tool to perform
high precision
work. The same is true for data. A good analyst is

cognizant of the types of analyses they can perform on various categories of data.

This is particularly true in statistical analysis, where there are often rules for the

types of analyses that can be performed on various types of data.

The standard characteristics that help us categorize data are presented in

Table 2.1. Each successive category permits greater
measurement
precision and

also permits more extensive statistical analysis. Thus, we can see from Table 2.1

that ratio data measurement is more precise than nominal data measurement. It is

important to remember that all these forms of data, regardless of their classiﬁcation,

are valuable, and we collect data in different forms by considering availability and

our analysis goals. For example, nominal data are used in many marketing studies,

while ratio data are more often the tools of ﬁnance, operations, and economics; yet,

all business functions collect data in each of these categories.

For nominal and ordinal data, we use non-metric measurement scales in the form

of categorical properties or attributes. Interval and ratio data are based on metric

measurement scales allowing a wide variety of mathematical operations to be per-

formed on the data. The major difference between interval and ratio measurement

scales is the existence of an absolute zero for ratio scales and arbitrary zero points for

interval scales. For example, consider a comparison of the Fahrenheit and Celsius

temperature scales. The zero points for these scales are arbitrarily set and do not

indicate an “absolute absence” of temperature. Similarly, it is incorrect to suggest

that 40
◦
Celsius is half as hot as 80
◦
Celsius. By contrast, it can be said that 16

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